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复杂系统生物学

2020-11-04T15:21:19Z

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复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref>。这样一个有机体、物种、“生态系统”等等的元系统定义 ,如自生系统理论中所述的系统的系统<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学 [[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-04T15:19:47Z

流峰：

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref>。这样一个有机体、物种、“生态系统”等等的元系统定义 ,如自生系统理论中所述的系统的系统<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学 [[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-04T15:19:15Z

流峰：

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]]。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref>。这样一个有机体、物种、“生态系统”等等的元系统定义 ,如自生系统理论中所述的系统的系统<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学 [[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:54:34Z

流峰：/* 另请参阅 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> <ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> ,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref><ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学 [[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:50:37Z

流峰：/* 生物体和生物圈的复杂性 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> <ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> ,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref><ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:49:51Z

流峰：/* 生物体和生物圈的复杂性 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> <ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> ,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，并不等同于自生系统理论中对系统中的系统的定义<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:47:16Z

流峰：/* 生物体和生物圈的复杂性 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref>。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref>。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref>，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，并不等同于自生系统理论中对系统中的系统的定义<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref>。它也不同于K·D·帕尔默在元系统工程中提出的定义<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref>，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref>。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> 实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:36:21Z

流峰：

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:35:30Z

流峰：

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref>，以及生命所必须的基本关系与关系模式<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref>。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:30:36Z

流峰：

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠；这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|一个复杂自适应系统的网络表示]]

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>。
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:26:35Z

流峰：/* 复杂系统生物学的主题 */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学 [[Quantum computer|Quantum biocomputation]]

* 量子遗传 [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>

* 关系生物学 Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>

* 自我繁殖(在更一般的情况下也称为自我复制) [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)

* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:24:46Z

流峰：/* 复杂系统生物学的主题 */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|一个DNA[[双螺旋]]的动画分子模型]]

[[File:Telomerase_illustration.jpg|thumb|center|296px|端粒酶的结构与功能]]

[[File:MAPKpathway_diagram.svg|thumb|center|140px|一个复杂的信号转导途径]]

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]s

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:21:21Z

流峰：/* 复杂系统生物学的主题 */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|center|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase_illustration.jpg|thumb|center|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway_diagram.svg|thumb|center|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* 细胞信号传导 [[Cell signaling]]

* 信号转导网络 [[Signal transduction|Signal transduction networks]]

* 复杂神经网络 [[neural net|Complex neural nets]]

* 基因网络 [[Gene network|Genetic networks]]

* 形态发生 [[Morphogenesis]]

* 数字形态发生 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]s

* 形态形成的拓扑模型 [[Catastrophe theory|Topological models of morphogenesis]]

* 渔业种群动态 [[Population dynamics of fisheries]]

* 流行病学 [[Epidemiology]]

* 理论生态学 [[Theoretical ecology]]

* 免疫系统 Immune system

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:19:07Z

流峰：/* 复杂系统生物学的主题 */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|mid|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase_illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway_diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:16:21Z

流峰：/* Further reading */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase_illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway_diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==进一步阅读==

*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

[[类别: 数学和理论生物学]]

[[类别: 生物信息学]]

[[类别: 流行病学]]

[[类别: 生物统计学]]

[[类别: 生物技术]]

[[范畴: 复杂系统理论]]

[[类别: 系统科学]]

复杂系统生物学

2020-11-03T15:14:35Z

流峰：/* References cited */

'''Complex systems biology''' ('''CSB''') is a branch or subfield of [[mathematical and theoretical biology]] concerned with [[complexity]] of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the [[interconnectivity|complex interactions]] of, and within, [[Biological network inference|bionetworks]],<ref>{{cite book |author1=Sprites, P |author2=Glymour, C |author3=Scheines, R |year=2000 |title= Causation, Prediction, and Search: Adaptive Computation and Machine Learning |edition=2nd |publisher=[[MIT Press]] |isbn=}}</ref> and on the fundamental relations and [[Relational algebra|relational patterns]] that are essential to life.<ref>Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.</ref><ref name="Bonner">Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.</ref><ref name="ReferenceA">{{cite journal | last1 = Rosen | first1 = R. | year = 1958a | title = A Relational Theory of Biological Systems | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 3| pages = 245–260 | doi=10.1007/bf02478302}}</ref><ref>{{cite journal | last1 = Baianu | first1 = I. C. | year = 2006 | title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 | doi=10.1007/s10516-005-4204-z| s2cid = 4673166 }}</ref><ref name="Rosen">{{cite journal | last1 = Rosen | first1 = R. | year = 1958b | title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 | doi=10.1007/bf02477890}}</ref> CSB is thus a field of theoretical sciences aimed at discovering and [[Relational model|modeling the relational patterns]] essential to life that has only a partial overlap with [[complex systems theory]],<ref name="springerlink">{{cite journal | last1 = Baianu | first1 = I. C. | last2 = Brown | first2 = R. | last3 = Glazebrook | first3 = J. F. | year = 2007 | title = Categorical Ontology of Complex Spacetime Structures: The Emergence of Life and Human Consciousness | url = | journal = Axiomathes | volume = 17 | issue = 3–4| pages = 223–352 | doi = 10.1007/s10516-007-9011-2 | citeseerx = 10.1.1.145.9486 | s2cid = 123179302 }}</ref> and also with the systems approach to biology called [[systems biology]]; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology.{{citation needed|date=June 2012}} Moreover, a wide range of abstract theoretical [[complex systems]] are studied as a field of [[applied mathematics]], with or without relevance to biology, chemistry or physics.

 复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。
A complete definition of [[complexity]] for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.<ref name="Bonner" /><ref>Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref>
[[File:Seawifs_global_biosphere.jpg|thumb|right]]

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

Most [[complex system]] models are often formulated in terms of concepts drawn from statistical physics, information theory and non-linear dynamics; however, such approaches are not focused on, or do not include, the conceptual part of complexity related to organization and topological attributes or algebraic topology, such as network connectivity of genomes, interactomes and biological organisms that are very important.<ref name="Rosen" /><ref>^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.</ref><ref>"abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.</ref> Recently, the two complementary approaches based both on [[information theory]], [[network topology]]/[[graph theory|abstract graph theory]] concepts are being combined for example in the fields of [[neuroscience]] and [[cognition|human cognition]].<ref name="springerlink" /><ref>http://hdl.handle.net/10101/npre.2011.6115.1 Wallace, Rodrick. When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness. Available from Nature Precedings (2011)</ref> It is generally agreed that there is a [[hierarchy]] of complexity levels of organization that should be considered as distinct from that of the levels of reality in [[ontology]].<ref name="springerlink" /><ref>{{cite journal | author = Poli R | year = 2001a | title = The Basic Problem of the Theory of Levels of Reality | url = | journal = Axiomathes | volume = 12 | issue = 3–4| pages = 261–283 | doi = 10.1023/A:1015845217681 | s2cid = 55743057 }}</ref><ref>{{cite journal | author = Poli R | year = 1998 | title = Levels | url = | journal = Axiomathes | volume = 9 | issue = 1–2| pages = 197–211 | doi=10.1007/bf02681712| pmid = 8053082 }}</ref> The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. </ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA_animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase_illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway_diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal| author = Faith, JJ| year = 2007| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles| journal = [[PLOS Biology]]| volume = 5 | issue = 1 | pages = 54–66| doi = 10.1371/journal.pbio.0050008| pmid = 17214507| pmc = 1764438| display-authors = 1| last2 = Hayete| first2 = Boris| last3 = Thaden| first3 = Joshua T.| last4 = Mogno| first4 = Ilaria| last5 = Wierzbowski| first5 = Jamey| last6 = Cottarel| first6 = Guillaume| last7 = Kasif| first7 = Simon| last8 = Collins| first8 = James J.| last9 = Gardner| first9 = Timothy S.}}</ref><ref name="Hayete2007">{{cite journal|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007| title = Size matters: network inference tackles the genome scale| journal = Molecular Systems Biology| volume = 3 |issue = 1| pages = 77| doi = 10.1038/msb4100118| pmid = 17299414| pmc = 1828748}}</ref>

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===参考文献引用===

* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992.

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004.

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005.

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987.

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001,

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 ; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 .

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003..

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991.

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience.

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975.

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984..

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001,

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917).

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:28:45Z

流峰：/* Notes */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==笔记==

{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:26:09Z

流峰：/* Biographies */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==传记==

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

==Notes==
笔记
{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:24:30Z

流峰：/* 另请参阅 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

==Biographies==
传记
{{colbegin}}

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

{{colend}}

==Notes==
笔记
{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:23:21Z

流峰：/* See also */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==另请参阅==

*数学和理论生物学 [[Mathematical and theoretical biology]]

*抽象关系生物学 [[Abstract relational biology]]

* 复杂 [[Complexity]]

* 复杂系统 [[Complex system]]

* 生物系统 [[Biological system]]

* 系统理论 [[Systems theory]]

* 动力系统 [[Dynamical system]]

* 动力系统理论 [[Dynamical systems theory]]

* 自动机理论 [[Automata theory]]

* 元胞自动机 [[Cellular automaton]]

* 系统生物学[[Systems biology]]

* 人类学中的系统理论 [[Systems theory in anthropology]]

* 自组织 [[Self-organization]]

* 非线性度 [[Nonlinearity]]

* 生成科学 [[Generative sciences]]

* 紧急情况 [[Emergence]]

* 生物圈 [[Biosphere]]

* 脱氧核糖核酸 [[DNA]]

* 量子生物学 [[Quantum biology]]

** 量子遗传学 [[Quantum mechanics|Quantum genetics]]

** 量子生物化学 [[Quantum chemistry|Quantum biochemistry]]

** 量子化学 [[Quantum chemistry]]

** 量子分子动力学 [[Molecular dynamics|Quantum molecular dynamics]]

* 蛋白质折叠 [[Protein folding]]

* 交互组学 [[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />

* 基因组学 [[Genomics]]

* 蛋白质组学 [[Proteomics]]

* 表观遗传学 [[Epigenetics]]

* 数字形态 [[Digital morphogenesis]]

* 复杂自适应系统 [[Complex adaptive system]]

* 多代理系统 [[Multi-agent system]]s

* 认知科学 [[Cognitive Science]]

* 面向模式的建模 [[Pattern oriented modeling]]

* 波动性，不确定性，复杂性和歧义性 [[Volatility, uncertainty, complexity and ambiguity]]

* 蓝色基因 [[blue Gene]]

* 折叠@家 [[Folding@home]]

* 端粒酶 [[Telomerase]]

* 生命是什么 [[What Is Life?]]

{{colend}}

==Biographies==
传记
{{colbegin}}

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

{{colend}}

==Notes==
笔记
{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:19:03Z

流峰：/* 复杂系统生物学的主题 */

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* 生物与物种的关系与进化 [[Organism]]s and [[species]] relations and evolution
* 物种间的相互作用 [[Intertidal ecology|Interactions among Species]]
* 进化论和种群遗传学 [[Evolution]] theories and [[population genetics]]
** 群体遗传学模型 [[Population genetics]] models
** 表观遗传学 [[Epigenetics]]
** 分子进化理论 [[Molecular evolution]] theories
* 量子生物计算学[[Quantum computer|Quantum biocomputation]]

* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* 计算基因模型 [[Computational gene|Computational gene models]]
** DNA拓扑学 [[DNA topology]]
** DNA测序理论 [[DNA sequencing theory]]
*进化发育生物学 [[Evolutionary developmental biology]]
* 自我生产 [[Autopoiesis]]
* 蛋白质折叠 [[Protein folding]]
* 端粒酶在体内的构象和功能 [[Telomerase]] conformations and functions ''in vivo''
* 表观遗传学 [[Epigenetics]]
* 交互组学 [[Interactomics]]<ref name="Faith2007">

{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==See also==
另请参阅
{{Portal|Systems science|Mathematics}}

{{Biological classification}}

{{colbegin}}

*[[Mathematical and theoretical biology]]
数学和理论生物学
*[[Abstract relational biology]]
抽象关系生物学
* [[Complexity]]
复杂
*[[Complex system]]
复杂系统
*[[Biological system]]
生物系统
* [[Systems theory]]
系统理论
* [[Dynamical system]]
动力系统
* [[Dynamical systems theory]]
动力系统理论
* [[Automata theory]]
自动机理论
* [[Cellular automaton]]
元胞自动机
*[[Systems biology]]
系统生物学
* [[Systems theory in anthropology]]
人类学中的系统理论
* [[Self-organization]]
自组织
* [[Nonlinearity]]
非线性度
* [[Generative sciences]]
生成科学
* [[Emergence]]
紧急情况
* [[Biosphere]]
生物圈
*[[DNA]]
脱氧核糖核酸
* [[Quantum biology]]
量子生物学
** [[Quantum mechanics|Quantum genetics]]
量子遗传学
** [[Quantum chemistry|Quantum biochemistry]]
量子生物化学
** [[Quantum chemistry]]
量子化学
** [[Molecular dynamics|Quantum molecular dynamics]]
量子分子动力学
*[[Protein folding]]
蛋白质折叠
*[[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />
交互组学
*[[Genomics]]
基因组学
*[[Proteomics]]
蛋白质组学
*[[Epigenetics]]
表观遗传学
* [[Digital morphogenesis]]
数字形态
* [[Complex adaptive system]]
复杂自适应系统
* [[Multi-agent system]]s
多代理系统
* [[Cognitive Science]]
认知科学
* [[Pattern oriented modeling]]
面向模式的建模
* [[Volatility, uncertainty, complexity and ambiguity]]
波动性，不确定性，复杂性和歧义性
* [[blue Gene]]
蓝色基因
* [[Folding@home]]
折叠@家
* [[Telomerase]]
端粒酶
* [[What Is Life?]]
生命是什么
{{colend}}

==Biographies==
传记
{{colbegin}}

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

{{colend}}

==Notes==
笔记
{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

<noinclude>

This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

[[Category:待整理页面]]

复杂系统生物学

2020-11-02T15:08:15Z

流峰：

复杂系统生物学 Complex system biology （CSB）是数学和理论生物学的一个分支或子领域，研究生物有机体结构和功能的复杂性，以及生物和物种的出现与进化，重点研究生物网络及其内部的复杂相互作用，以及生命所必须的基本关系与关系模式。因此，CBS是一个旨在发现和建模生命所必需的关系模式理论科学领域，它只与复杂系统理论和被称为系统生物学的生物学的系统方法有部分重叠;这是因为后者主要局限于生物组织和有机体的简化模型，以及对与生物学复杂性相关的哲学或语义问题的一般性考虑。此外，人们把广泛的抽象理论复杂系统作为应用数学的一个领域进行研究，无论其是否与生物学、化学或物理相关。

[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
一个复杂自适应系统的网络表示

==生物体和生物圈的复杂性==

研究人员一直困扰于如何完整定义单个生物体、物种、生态系统、生物进化和生物圈的复杂性，而且这仍然是一个悬而未决的问题。

大多数复杂系统模型通常是根据统计物理学、信息论和非线性动力学的概念来制定的；这些方法并不关注或者说不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如基因组、交互体和生物有机体的网络连通性这些重要概念。近年来，人们把以信息论、网络拓扑/抽象图论概念为基础的两种互补方法在神经科学和人类认知等领域结合起来。人们普遍认为，组织的复杂程度存在一种应与本体论的现实层次相区别层次结构，现代等级分类的分类方法也承认生物圈的例如:生物领域和生物圈、生物的界、门、纲、目、科、属和种等复杂层次结构。由于生物体具有动态性和组成的可变性、内在的“模糊性”、自生属性、自我繁殖的能力等等，生物体不符合一般系统的“标准”的定义，因此它们在功能和结构上都是“超级复杂”的；因此，在CSB中，生物体只能被定义为一种简单动态系统，“元系统”。这样一个有机体、物种、“生态系统”等等的元系统定义，并不等同于自生系统理论中对系统中的系统的定义。它也不同于K·D·帕尔默在元系统工程中提出的定义，即生物体不同于具有固定输入输出转换函数的机器和自动机，或不同于具有固定相空间的连续动力系统，这与笛卡尔哲学思想相反；因此，尽管“非确定性自动机”和“模糊自动机”也被定义了，但生物体不能仅仅用五组a（状态、启动状态、输入和输出集/字母、转换函数）来定义。然而，棋盘自动机 tessellation automata或元胞自动机 cellular automata 提供了一种直观的、可视化的/计算的视角来洞察较低层次的复杂性，因此已经成为一种越来越流行的离散模型，研究领域包括可计算理论、应用数学、物理、计算机科学、理论生物学/系统生物学、癌症模拟和微观结构建模。利用遗传算法实现元胞自动机是一个桥接棋盘自动机和CSB中的高层次复杂性方法之间差距的新兴领域。

of taxonomic ranks, such as: [[domain (biology)|biological domain]] and biosphere, [[Kingdom (biology)|biological kingdom]], [[Phylum]], [[Class (biology)|biological class]], [[Order (biology)|order]], [[Family (biology)|family]], [[genus]] and [[species]]. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as '[[meta-system]]s' of simpler dynamic systems<ref name="springerlink" /><ref>[http://pespmc1.vub.ac.be/MST.html Metasystem Transition Theory], [[Valentin Turchin]], [[Cliff Joslyn]], 1993-1997</ref> Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a ''system of systems'' as in [[Autopoiesis|Autopoietic System]]s Theory,;<ref>[http://archonic.net Reflexive Autopoietic Systems Theory]</ref> it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,<ref>[http://archonic.net/incosewg/ppframe.htm Meta-system Engineering], Kent D. Palmer, 1996</ref> organisms being quite different from machines and [[Automata theory|automata]] with fixed input-output transition functions, or a continuous [[dynamical system]] with fixed [[phase space]],<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

of taxonomic ranks, such as: biological domain and biosphere, biological kingdom, Phylum, biological class, order, family, genus and species. Because of their dynamic and composition variability, intrinsic "fuzziness", autopoietic attributes, ability to self-reproduce, and so on, organisms do not fit into the 'standard' definition of general systems, and they are therefore 'super-complex' in both their function and structure; organisms can be thus be defined in CSB only as 'meta-systems' of simpler dynamic systems Such a meta-system definition of organisms, species, 'ecosystems', and so on, is not equivalent to the definition of a system of systems as in Autopoietic Systems Theory,; it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering, organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,<ref>Hoff, M.A., Roggia, K.G., Menezes, P.B.:(2004). Composition of Transformations: A

Framework for Systems with Dynamic Topology. ''International Journal of Computing Anticipatory System's'' 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple ''A'' of ''(states, startup state, input and output sets/alphabet, transition function)'',<ref>[[John E. Hopcroft]], [[Rajeev Motwani]], [[Jeffrey D. Ullman]].2000. [[Introduction to Automata Theory, Languages, and Computation]] (2nd Edition)Pearson Education. {{ISBN|0-201-44124-1}}</ref> although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or [[cellular automaton|cellular automata]] provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>The Evolutionary Design of Collective Computation in Cellular Automata, James P. Crutchfeld, Melanie Mitchell, Rajarshi Das (In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics|Exploring the Interplay of Selection, Neutrality, Accident, and Function. New York: Oxford University Press, 2002.)</ref><ref>

Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270</ref> contrary to the Cartesian philosophical thinking; thus, organisms cannot be defined merely in terms of a quintuple A of (states, startup state, input and output sets/alphabet, transition function), although 'non-deterministic automata', as well as 'fuzzy automata' have also been defined. Tessellation or cellular automata provide however an intuitive, visual/computational insight into the lower levels of complexity, and have therefore become an increasingly popular, discrete model studied in computability theory, applied mathematics, physics, computer science, theoretical biology/systems biology, cancer simulations and microstructure modeling. Evolving cellular automata using genetic algorithms<ref>

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref><ref>{{cite journal | doi = 10.1073/pnas.0307811100 | last1 = Peak | first1 = West | last2 = Messinger | first2 = Mott | year = 2004 | title = Evidence for complex, collective dynamics and emergent, distributed computation in plants | journal = Proceedings of the National Academy of Sciences of the USA | volume = 101 | issue = 4| pages = 918–922 |bibcode = 2004PNAS..101..918P | pmid = 14732685 | last3 = Messinger | first3 = SM | last4 = Mott | first4 = KA | pmc = 327117}}</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work, Melanie Mitchell, James P. Crutchfeld, Rajarshi Das (In Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA'96). Moscow, Russia: Russian Academy of Sciences, 1996.)</ref> is also an emerging field attempting to bridge the gap between the tessellation automata and the higher level complexity approaches in CSB.

==复杂系统生物学的主题==

[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]

一个DNA[[双螺旋]]的动画分子模型

[[File:Telomerase illustration.jpg|thumb|right|296px|Telomerase structure and function]]

端粒酶的结构与功能

[[File:MAPKpathway diagram.svg|thumb|right|140px|A Complex Signal Transduction Pathway]]

一个复杂的信号转导途径

以下只是复杂系统生物学所涵盖的部分主题列表:

* [[Organism]]s and [[species]] relations and evolution
生物与物种的关系与进化
* [[Intertidal ecology|Interactions among Species]]
物种间的相互作用
* [[Evolution]] theories and [[population genetics]]
进化论和种群遗传学
** [[Population genetics]] models
群体遗传学模型
** [[Epigenetics]]
表观遗传学
** [[Molecular evolution]] theories
分子进化理论
* [[Quantum computer|Quantum biocomputation]]
量子生物计算学
* [[DNA computing|Quantum genetics]]<ref>{{Cite journal | doi = 10.1007/BF02478347 | author = Rosen, R. 1960. | year = 1960| title = A quantum-theoretic approach to genetic problems | url = | journal = Bulletin of Mathematical Biophysics | volume = 22 | issue = 3| pages = 227–255 }}</ref>
量子遗传
* Relational biology<ref name="ReferenceA"/><ref>{{Cite journal | doi = 10.1007/s10516-005-4204-z | author = Baianu, I. C.: 2006 | year = 2006| title = Robert Rosen's Work and Complex Systems Biology | url = | journal = Axiomathes | volume = 16 | issue = 1–2| pages = 25–34 }}</ref><ref>{{Cite journal | doi = 10.1007/BF02477890 | author = Rosen, R.: 1958b | year = 1958| title = The Representation of Biological Systems from the Standpoint of the Theory of Categories | url = | journal = Bulletin of Mathematical Biophysics | volume = 20 | issue = 4| pages = 317–341 }}</ref><ref>[http://planetmath.org/?op=getobj&from=objects&id=10921 PlanetMath]</ref>
关系生物学
* [[DNA|Self-reproduction]]<ref>{{cite web|url=http://planetmath.org/?method=l2h&from=objects&name=NaturalTransformationsOfOrganismicStructures&op=getobj|title=PlanetMath |publisher=PlanetMath |date= |accessdate=2010-03-17}}</ref> (also called [[self-replication]] in a more general context)
自我繁殖(在更一般的情况下也称为自我复制)
* [[Computational gene|Computational gene models]]
计算基因模型
** [[DNA topology]]
DNA拓扑学
** [[DNA sequencing theory]]
DNA测序理论
*[[Evolutionary developmental biology]]
进化发育生物学
* [[Autopoiesis]]
自我生产
* [[Protein folding]]
蛋白质折叠
* [[Telomerase]] conformations and functions ''in vivo''
端粒酶在体内的构象和功能
* [[Epigenetics]]
表观遗传学
* [[Interactomics]]<ref name="Faith2007">
交互组学
{{cite journal

{{cite journal

{引用期刊

| author = Faith, JJ

| author = Faith, JJ

作者 = Faith，JJ

| year = 2007

| year = 2007

2007年

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

| title = 大比例绘制和验证表达式概要中的大肠桿菌转录调控

| journal = [[PLOS Biology]]

| journal = PLOS Biology

| journal = PLOS Biology

| volume = 5 | issue = 1 | pages = 54–66

| volume = 5 | issue = 1 | pages = 54–66

5 | issue = 1 | pages = 54-66

| doi = 10.1371/journal.pbio.0050008

| doi = 10.1371/journal.pbio.0050008

10.1371/journal.pbio. 0050008

| pmid = 17214507

| pmid = 17214507

17214507

| pmc = 1764438

| pmc = 1764438

1764438

| display-authors = 1

| display-authors = 1

| display-authors = 1

| last2 = Hayete

| last2 = Hayete

2 = Hayete

| first2 = Boris

| first2 = Boris

2 = Boris

| last3 = Thaden

| last3 = Thaden

3 = Thaden

| first3 = Joshua T.

| first3 = Joshua T.

3 = Joshua t.

| last4 = Mogno

| last4 = Mogno

4 = Mogno

| first4 = Ilaria

| first4 = Ilaria

4 = Ilaria

| last5 = Wierzbowski

| last5 = Wierzbowski

5 = Wierzbowski

| first5 = Jamey

| first5 = Jamey

5 = Jamey

| last6 = Cottarel

| last6 = Cottarel

6 = Cottarel

| first6 = Guillaume

| first6 = Guillaume

6 = Guillaume

| last7 = Kasif

| last7 = Kasif

7 = Kasif

| first7 = Simon

| first7 = Simon

7 = Simon

| last8 = Collins

| last8 = Collins

8 = Collins

| first8 = James J.

| first8 = James J.

8 = James j.

| last9 = Gardner

| last9 = Gardner

9 = Gardner

| first9 = Timothy S.

| first9 = Timothy S.

9 = Timothy s.

}}</ref><ref name="Hayete2007">

}}</ref><ref name="Hayete2007">

} </ref > < ref name ="hayete2007">

{{cite journal

{{cite journal

{引用期刊

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

|author1=Hayete, B |author2=Gardner, TS |author3=Collins, JJ | year = 2007

1 = Hayete，b | author2 = Gardner，TS | author3 = Collins，JJ | year = 2007

| title = Size matters: network inference tackles the genome scale

| title = Size matters: network inference tackles the genome scale

| title = 规模问题: 网络推理解决了基因组规模

| journal = [[Molecular Systems Biology]]

| journal = Molecular Systems Biology

分子系统生物学

| volume = 3 |issue = 1| pages = 77

| volume = 3 |issue = 1| pages = 77

3 | issue = 1 | pages = 77

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| doi = 10.1038/msb4100118

| pmid = 17299414

| pmid = 17299414

17299414

| pmc = 1828748

| pmc = 1828748

1828748

}}</ref>

}}</ref>

} </ref >

* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
理论生态学
* Immune system
免疫系统

==See also==
另请参阅
{{Portal|Systems science|Mathematics}}

{{Biological classification}}

{{colbegin}}

*[[Mathematical and theoretical biology]]
数学和理论生物学
*[[Abstract relational biology]]
抽象关系生物学
* [[Complexity]]
复杂
*[[Complex system]]
复杂系统
*[[Biological system]]
生物系统
* [[Systems theory]]
系统理论
* [[Dynamical system]]
动力系统
* [[Dynamical systems theory]]
动力系统理论
* [[Automata theory]]
自动机理论
* [[Cellular automaton]]
元胞自动机
*[[Systems biology]]
系统生物学
* [[Systems theory in anthropology]]
人类学中的系统理论
* [[Self-organization]]
自组织
* [[Nonlinearity]]
非线性度
* [[Generative sciences]]
生成科学
* [[Emergence]]
紧急情况
* [[Biosphere]]
生物圈
*[[DNA]]
脱氧核糖核酸
* [[Quantum biology]]
量子生物学
** [[Quantum mechanics|Quantum genetics]]
量子遗传学
** [[Quantum chemistry|Quantum biochemistry]]
量子生物化学
** [[Quantum chemistry]]
量子化学
** [[Molecular dynamics|Quantum molecular dynamics]]
量子分子动力学
*[[Protein folding]]
蛋白质折叠
*[[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />
交互组学
*[[Genomics]]
基因组学
*[[Proteomics]]
蛋白质组学
*[[Epigenetics]]
表观遗传学
* [[Digital morphogenesis]]
数字形态
* [[Complex adaptive system]]
复杂自适应系统
* [[Multi-agent system]]s
多代理系统
* [[Cognitive Science]]
认知科学
* [[Pattern oriented modeling]]
面向模式的建模
* [[Volatility, uncertainty, complexity and ambiguity]]
波动性，不确定性，复杂性和歧义性
* [[blue Gene]]
蓝色基因
* [[Folding@home]]
折叠@家
* [[Telomerase]]
端粒酶
* [[What Is Life?]]
生命是什么
{{colend}}

==Biographies==
传记
{{colbegin}}

* [[Charles Darwin]]

* [[D'Arcy Thompson]]

* [[William Ross Ashby]]

* [[Ludwig von Bertalanffy]]

* [[Ronald Brown (mathematician)|Ronald Brown]]

* [[Joseph Fourier]]

* [[Brian Goodwin]]

* [[George Karreman]]

* [[Charles S. Peskin]]

* [[Nicolas Rashevsky]]

* [[Robert Rosen (theoretical biologist)|Robert Rosen]]

* [[Anatol Rapoport]]

* [[Rosalind Franklin]]

* [[Francis Crick]]

* [[René Thom]]

* [[Vito Volterra]]

* [[Norbert Wiener]]

{{colend}}

==Notes==
笔记
{{Reflist|colwidth=30em}}

===References cited===
参考文献引用
* {{cite journal | last1 = Ahmed | first1 = E. | year = 2006 | title = On modeling the immune system as a complex system | url = | journal = Theor. BioSci. | volume = 124 | issue = 3–4| pages = 413–8 | bibcode = 2008arXiv0801.0847A | arxiv = 0801.0847 | doi = 10.1016/j.thbio.2005.07.001 | pmid = 17046369 }}

* Baianu, I. C., Computer Models and Automata Theory in Biology and Medicine., ''Monograph'', Ch.11 in M. Witten (Editor), ''Mathematical Models in Medicine'', vol. 7., Vol. 7: 1513-1577 (1987),Pergamon Press:New York, (updated by Hsiao Chen Lin in 2004 {{ISBN|0-08-036377-6}}

* Bonner, J. T. 1988. ''The Evolution of Complexity by Means of Natural Selection''. Princeton: Princeton University Press.

* Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", ''Complexity'', vol. 13, no. 6, July/August 2008.

* Drazin, P.G., ''Nonlinear systems''. [[Cambridge University Press|C.U.P.]], 1992. {{ISBN|0-521-40668-4}}

* Edelstein-Keshet, L., ''Mathematical Models in Biology''. SIAM, 2004. {{ISBN|0-07-554950-6}}

* Forgacs, G.; S. A. Newman, ''Biological Physics of the Developing Embryo''. C.U.P., 2005. {{ISBN|0-521-78337-2}}

* {{cite journal |author=Israel G |title=On the contribution of Volterra and Lotka to the development of modern biomathematics |journal=History and Philosophy of the Life Sciences |volume=10 |issue=1 |pages=37–49 |year=1988 |pmid=3045853}}

* Israel, G., 2005, "Book on mathematical biology" in [[Ivor Grattan-Guinness|Grattan-Guinness, I.]], ed., ''Landmark Writings in Western Mathematics''. Elsevier: 936-44.

* Jordan, D.W.; Smith, P., ''Nonlinear ordinary differential equations'', 2nd ed. O.U.P., 1987. {{ISBN|0-19-856562-3}}

* Kampen, N.G. van. ''Stochastic Processes in Physics and Chemistry'', North Holland., 3rd ed. 2001, {{ISBN|0-444-89349-0}}

* Murray, J.D., ''Mathematical Biology''. Springer-Verlag, 3rd ed. in 2 vols.: ''Mathematical Biology: I. An Introduction'', 2002 {{ISBN|0-387-95223-3}}; ''Mathematical Biology: II. Spatial Models and Biomedical Applications'', 2003 {{ISBN|0-387-95228-4}}.

* Nicolas Rashevsky. (1938)., ''Mathematical Biophysics''. Chicago: University of Chicago Press.

* Preziosi, L., ''Cancer Modelling and Simulation''. Chapman Hall/CRC Press, 2003. {{ISBN|1-58488-361-8}}.

* Renshaw, E., ''Modelling biological populations in space and time''. C.U.P., 1991. {{ISBN|0-521-44855-7}}

* Rosen, Robert.1991, ''Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life'', Columbia University Press, published posthumously:

* Rosen, Robert .1970. ''Dynamical system theory in biology''. New York, Wiley-Interscience. {{ISBN|0-471-73550-7}}

* Rosen, Robert. 2000, ''Essays on Life Itself'', Columbia University Press.

* Rosen, Robert. 2003, "''Anticipatory Systems; Philosophical, Mathematical, and Methodolical Foundations''", Rosen Enterprises publs.

* Rubinow, S.I., ''Introduction to mathematical biology''. John Wiley, 1975. {{ISBN|0-471-74446-8}}

* {{cite journal |author=Scudo FM |title=Vito Volterra and theoretical ecology |journal=Theoretical Population Biology |volume=2 |issue=1 |pages=1–23 |date=March 1971 |pmid=4950157 |doi=10.1016/0040-5809(71)90002-5}}

* Segel, L.A., ''Modeling dynamic phenomena in molecular and cellular biology''. C.U.P., 1984. {{ISBN|0-521-27477-X}}.

* Strogatz, S.H., ''Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering.'' Perseus, 2001, {{ISBN|0-7382-0453-6}}

* Thompson, D'Arcy W., 1992. ''On Growth and Form''. Dover reprint of 1942, 2nd ed. (1st ed., 1917). {{ISBN|0-486-67135-6}}

==Further reading==
进一步阅读
*[https://web.archive.org/web/20160119231346/http://www.kli.ac.at/theorylab/index.html A general list of Theoretical biology/Mathematical biology references, including an updated list of actively contributing authors].

*[http://planetmath.org/?method=l2h&from=objects&id=10746&op=getobj A list of references for applications of category theory in relational biology].

*[http://www.people.vcu.edu/~mikuleck/rosen.htm An updated list of publications of theoretical biologist Robert Rosen]

* [http://homepage.uibk.ac.at/~c720126/humanethologie/ws/medicus/block1/inhalt.html Theory of Biological Anthropology (Documents No. 9 and 10 in English)]

* [http://www.scientistsolutions.com/t5844-Drawing+the+line+between+Theoretical+and+Basic+Biology.html Drawing the Line Between Theoretical and Basic Biology], a forum article by Isidro A. T. Savillo

* {{cite journal|title=Synthesis and Analysis of a Biological System|journal=Genome Informatics Series|number=Sers 10|pages=352–353|oclc=203735966|year=1999|last1=Kurata|first1=Hiroyuki|last2=Taira|first2=K|last3=Kitano|first3=H}}

{{Systems}}

[[Category:Mathematical and theoretical biology]]

Category:Mathematical and theoretical biology

类别: 数学和理论生物学

[[Category:Bioinformatics]]

Category:Bioinformatics

类别: 生物信息学

[[Category:Epidemiology]]

Category:Epidemiology

类别: 流行病学

[[Category:Biostatistics]]

Category:Biostatistics

类别: 生物统计学

[[Category:Biotechnology]]

Category:Biotechnology

类别: 生物技术

[[Category:Complex systems theory]]

Category:Complex systems theory

范畴: 复杂系统理论

[[Category:Systems science]]

Category:Systems science

类别: 系统科学

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This page was moved from [[wikipedia:en:Complex systems biology]]. Its edit history can be viewed at [[复杂系统生物学/edithistory]]</noinclude>

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人工生命历史

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人工生命历史

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自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== 计算机出现之前 ==

[[自动装置]]是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref>。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref>。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> 。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>它在整个欧洲展出，直至其年久失修。

然而，直到廉价计算力的发明，[[人工生命]]才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950-1970年代==

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才[[约翰·冯·诺依曼]](John von Neumann)。20世纪40年代末，[[莱纳斯·鲍林]]（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“[[自动机]]”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与[[Stanislaw Ulam]]一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个[[细胞自动机]]（[[元胞自动机]]、[[格状自动机]]）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像[[艾伦·图灵]]定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯·诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

20世纪50年代，[[霍默•雅各布森]](Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[爱德华·摩尔]](Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。[[弗里曼·戴森]](Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

20世纪60年代，剑桥大学(University of Cambridge)教授[[约翰·霍顿·康威]](John Horton Conway)发明了最著名的细胞自动机。他称之为“[[生命游戏]]”，并通过[[《科学美国人》]]杂志的[[马丁·加德纳]]专栏进行宣传。

==1970-1980年代==

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者[[亚瑟·伯克]](Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家[[查尔斯·桑德斯·皮尔斯]]（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生[[托马索·托福利]](Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[克里斯托弗·兰顿]]（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和[[埃德加·科德]]（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:20:08Z

流峰：

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== 计算机出现之前 ==

[[自动装置]]是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref>。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref>。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> 。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>它在整个欧洲展出，直至其年久失修。

然而，直到廉价计算力的发明，[[人工生命]]才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950-1970年代==

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才[[约翰·冯·诺依曼]](John von Neumann)。20世纪40年代末，[[莱纳斯·鲍林]]（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“[[自动机]]”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与[[Stanislaw Ulam]]一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个[[细胞自动机]]（[[元胞自动机]]、[[格状自动机]]）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像[[艾伦·图灵]]定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯·诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

20世纪50年代，[[霍默•雅各布森]](Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:13:42Z

流峰：/* 计算机出现之前 */

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== 计算机出现之前 ==

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref>。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref>。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:09:38Z

流峰：/* Pre-computer */

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== 计算机出现之前 ==

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:09:07Z

流峰：

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== Pre-computer ==
计算机出现之前

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:08:43Z

流峰：

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether [[Pygmalion (mythology)|Pygmalion]] or [[Frankenstein]], humanity has been fascinated with the idea of [[artificial life]].

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是[[《皮格马利翁》]]还是[[《弗兰肯斯坦》]]，人类一直对 '''人工生命artificial life'''的概念着迷。

== Pre-computer ==
计算机出现之前

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-21T13:07:00Z

流峰：

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether [[Pygmalion (mythology)|Pygmalion]] or [[Frankenstein]], humanity has been fascinated with the idea of [[artificial life]].

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是《皮格马利翁》还是《科学怪人》，人类一直对 '''人工生命artificial life'''的概念着迷。

== Pre-computer ==
计算机出现之前

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

[[Category:待整理页面]]

人工生命历史

2020-10-20T15:41:02Z

流峰：/* Pre-computer */

已由吕倩倩初步翻译

{{Refimprove|date=January 2007}}

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether [[Pygmalion (mythology)|Pygmalion]] or [[Frankenstein]], humanity has been fascinated with the idea of [[artificial life]].

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories. Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.

自从人类开始记录他们的神话和故事以来，人工制品被赋予生命的想法就一直吸引着人类。无论是《皮格马利翁》还是《科学怪人》，人类一直对 '''人工生命artificial life'''的概念着迷。

== Pre-computer ==
计算机出现之前

[[Automaton]]s were quite a novelty. In the days before computers and electronics, some were very sophisticated, using [[pneumatics]], [[mechanics]], and [[hydraulics]]. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of [[Hero of Alexandria]], which included sophisticated mechanical and hydraulic solutions.<ref>Droz, Edmond. (April 1962), From joined doll to talking robot, New Scientist, vol. 14, no. 282. pp. 37–40.</ref> Many of his notable works were included in the book ''Pneumatics'', which was also used for constructing machines until early modern times.<ref>{{Cite book|title=Synthetic Biology Analysed: Tools for Discussion and Evaluation|last=Engelhard|first=Margret|publisher=Springer|year=2016|isbn=9783319251431|location=Cham|pages=75}}</ref> In 1490, Leonardo da Vinci also constructed an [[Leonardo's robot|armored knight]], which is considered the first humanoid robot in Western civilization.<ref>{{Cite book|title=Introduction to Mobile Robot Control|last=Tzafestas|first=Spyros|publisher=Elsevier|year=2014|isbn=9780124170490|location=Waltham, MA|pages=3}}</ref>

Automatons were quite a novelty. In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

自动装置是相当新奇的东西。在计算机和电子设备出现之前，一些自动装置设备非常复杂，涉及气体力学、机械力学和水力学。第一个自动机诞生于公元前三世纪到公元前二世纪之间，由亚历山大港的希罗发明，其中包括复杂的机械和液压解决方案。希罗的许多著名作品被收录在《气体力学》一书中，直到近代早期，这本书还被用于建造机器。1490年，列奥纳多·达·芬奇还建造了一个装甲骑士，这被认为是西方文明中第一个人形机器人。

Other early famous examples include [[al-Jazari]]'s [[humanoid robot]]s. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music.<ref>{{Cite book|title=Science Year by Year , Dorling Kindersley, 2013: Science Year by Year|last=Winston|first=Robert|publisher=DK|year=2013|isbn=9781409316138|location=London|pages=334}}</ref> There is also the case of [[Jacques de Vaucanson]]'s [[digesting Duck|artificial duck]] exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system.<ref>{{Cite book|title=Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis, 2nd edition|last=Deutsch|first=Andreas|publisher=Birkhäuser|year=2018|isbn=9781489979780|location=New York|pages=67}}</ref> The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.<ref>{{cite web| url=http://www.nyu.edu/pages/linguistics/courses/v610051/gelmanr/ling.html| title=Gallery of Automata| accessdate=2006-03-03| first=Rony| last=Gelman}}</ref>

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that [[artificial life]] as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

然而，直到廉价计算力的发明，人工生命才真正成为一门合法的科学，能够更多地沉浸在理论和计算中，而非仅存在于机械和神话里。

==1950s–1970s==
1950-1970年代

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]]. At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of [[self-replicating machine]]s. He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]]. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]]. This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

[[Homer Jacobson]] illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

20世纪50年代，霍默•雅各布森(Homer Jacobson)用一组模型火车说明了基本的自我复制——一个由“头”和“尾”车厢组成的种子“有机体”，只要有一个可供提取的新车厢随机池，就可以使用系统的简单规则，持续创造出与自身相同的新“有机体”。

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor [[John Horton Conway]] invented the most famous cellular automaton in the 1960s. He called it the [[Conway's Game of Life|Game of Life]], and publicized it through [[Martin Gardner]]'s column in ''[[Scientific American]]'' magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

20世纪60年代，剑桥大学(University of Cambridge)教授约翰·霍顿·康威(John Horton Conway)发明了最著名的细胞自动机。他称之为“生命游戏”，并通过《科学美国人》杂志的马丁·加德纳专栏进行宣传。

==1970s–1980s==
1970-1980年代

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]]. He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。
在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

[[Christopher Langton]] was an unconventional researcher, with an undistinguished academic career that led him to a job programming [[Digital Equipment Corporation|DEC]] mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of [[Edgar F. Codd]], who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an [[Apple II]] desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:<ref>Langton, C.G. (1989) "Artificial Life", in ''Artificial Life'', Langton (ed), (Addison-Wesley:Reading, MA) page 1.</ref>

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>''Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.''</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems.</blockquote>

人工生命是对具有自然生命系统行为特征的人工系统的研究。它寻求解释生命的任何可能表现形式，而不局限于地球上已经演化出来的特殊例子。这包括生物和化学实验、计算机模拟和纯理论研究。发生在分子、社会和进化尺度上的过程需要进行研究。最终目标是提取出生命系统的逻辑形式。

<blockquote>''Microelectronic technology and genetic engineering will soon give us the capability to create new life forms ''in silico'' as well as ''in vitro''. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.''</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

微电子技术和基因工程将很快使我们有能力在硅片中和在体外创造新的生命形式。这种能力将给人类带来有史以来最深远的技术、理论和伦理挑战。对于那些试图模拟或合成生命系统各方面的人来说，现在似乎是合适的时机。

[[Ed Fredkin]] founded the Information Mechanics Group at [[Massachusetts Institute of Technology|MIT]], which united Toffoli, [[Norman Margolus]], [[Gerard Vichniac]], and [[Charles H. Bennett (computer scientist)|Charles Bennett]]. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

艾埃德·弗雷德金(Ed Fredkin)在麻省理工学院(MIT)成立了信息力学小组，由托福利(Toffoli)、诺曼·马格卢斯(Norman Margolus)、杰拉德·维克尼亚克(Gerard Vichniac)和查尔斯·贝内特(Charles Bennett)组成。这个小组创造了一台专门用来执行细胞自动机的计算机，并最终将其缩小到一块电路板的大小。这种“细胞自动机”使得那些买不起复杂计算机的科学家也能够进行大量的人工生命研究。

In 1982, computer scientist named [[Stephen Wolfram]] turned his attention to cellular automata. He explored and categorized the types of [[complexity]] displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

1982年，计算机科学家斯蒂芬·沃尔夫拉姆（Stephen Wolfram）将他的注意力转向细胞自动机。他探索和分类了一维CAs所显示的复杂性类型，并展示了它们如何应用于自然现象，如贝壳的模式和植物生长的性质。

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

诺曼·帕卡德(Norman Packard)在高级研究院与沃尔夫拉姆一起工作，他使用CAs来模拟雪花的生长，遵循非常基本的规则。

Computer animator [[Craig Reynolds (computer graphics)|Craig Reynolds]] similarly used three simple rules to create recognizable [[Flocking (behavior)|flocking]] behaviour in a [[Boids|computer program]] in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. [[Computer animation]] has continued to be a key commercial driver of alife research as the creators of [[Film|movie]]s attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

1987年，计算机动画师克雷格·雷诺兹（Craig Reynolds）同样使用三个简单的规则在一个计算机程序中创建了可识别的群集行为。由于完全没有自顶向下的编程，群集体产生了类似生命体的解决方案，以避开摆在他们道路上的障碍。计算机动画一直是人工生命研究的主要商业驱动力，因为电影的创作者试图寻找更现实和廉价的方式，来使自然形式如植物、动物运动、毛发生长和复杂的有机材质等具有生命力。

[[J. Doyne Farmer]] was a key figure in tying artificial life research to the emerging field of [[complex adaptive system]]s, working at the [[Center for Nonlinear Studies]] (a basic research section of [[Los Alamos National Laboratory]]), just as its star chaos theorist [[Mitchell Feigenbaum]] was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving. Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==
2000年

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol.7,1-52(1964) . / ref Axelrod，r. & Hamilton，W.d. 合作的演变。科学211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 101000 strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过101000种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions{{Clarify|date=July 2010}}  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter{{Clarify|date=July 2010}} in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions  expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase  in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。

In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。

Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].

Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。

== See also ==
参见
* [[Automaton]]
自动机
* [[Clanking replicator]]
自我复制器
* [[Cellular automaton]]
元胞自动机

==References==
参考文献

<references />

==External links==
外部链接

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008

Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014).人工生命的过去、现在和未来。机器人和人工智能的前沿，1(8)。Https://dx.doi.org/10.3389/frobt.2014.00008

[[Category:Artificial life]]

Category:Artificial life

类别: 人工生命

[[Category:History of biology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of technology|Artificial life, history of]]

Artificial life, history of

人工生命的历史

[[Category:History of artificial intelligence|Artificial life, history of]]

Artificial life, history of

人工生命的历史

<noinclude>

This page was moved from [[wikipedia:en:History of artificial life]]. Its edit history can be viewed at [[人工生命历史/edithistory]]</noinclude>

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