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第27行： − 复杂适应性系统的网络表示+ + 第37行：
第38行： − 对于单个生物体、物种、生态系统、生物进化和生物圈的复杂性的完整定义一直困扰着研究人员，而且仍然是一个持续的问题+ 第45行：
第46行： − 大多数复杂系统模型通常是根据从统计物理学、信息论和非线性动力学中提取的概念来建立的; 然而，这些方法并不关注或不包括与组织和拓扑属性或代数拓扑相关的复杂性的概念部分，例如基因组的网络连接、交叉点和非常重要的生物有机体。近年来，以信息论为基础的网络拓扑/抽象图论两种互补的研究方法在神经科学和人类认知领域得到了广泛的应用。人们普遍认为，组织的复杂性层次有一个层次，应该被视为不同于本体论中的实在层次。在现代分类中，生物圈内组织的复杂程度等级也得到承认+ 第51行：
第52行： − 分类等级，如: 生物领域和生物圈，生物界，门，生物纲，目，科，属和种。由于生物体具有动态性和组成的可变性、固有的“模糊性”、自创生属性、自我繁殖能力等等，生物体不符合一般系统的“标准”定义，因此它们在功能和结构上都是“超复杂”的; 因此，在 CSB 中，生物体只能被定义为简单动态系统的“元系统”。在元系统工程中，有机体与具有固定输入输出转换函数的机器和自动机，或具有固定相空间的连续动力系统有很大的不同。变换的组合: a 第57行：
第57行： − 动态拓扑系统框架。国际计算期刊预期系统的14:259-270 </ref </ref > 与笛卡尔哲学思想相反; 因此，生物体不能仅仅被定义为五个 a (状态，启动状态，输入和输出集/字母表，转换函数) ，虽然“非确定性自动机” ，以及“模糊自动机”也已经被定义。镶嵌或细胞自动机提供了一个直观的，可视的/可计算的洞察力的复杂性较低的水平，因此已成为一个越来越流行的，离散模型研究在可计算性理论，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。使用遗传算法进化细胞自动机 第63行：
第62行： − 基于遗传算法的细胞自动机的演化: 最新研究回顾，Melanie Mitchell，James p. Crutchfeld，Rajarshi Das (In Proceedings of the First International Conference o n n x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x。俄罗斯，莫斯科: 俄罗斯科学院，1996年。) </ref > 也是一个新兴的领域，它试图弥合分块自动机和 CSB 中更高层次复杂性方法之间的差距。 + 第91行：
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无编辑摘要
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 complex interactions of, and within, bionetworks, and on the fundamental relations and relational patterns that are essential to life. CSB is thus a field of theoretical sciences aimed at discovering and modeling the relational patterns essential to life that has only a partial overlap with complex systems theory, 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. 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.
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 complex interactions of, and within, bionetworks, and on the fundamental relations and relational patterns that are essential to life. CSB is thus a field of theoretical sciences aimed at discovering and modeling the relational patterns essential to life that has only a partial overlap with complex systems theory, 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. 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 systems biology，CSB)是数学和理论生物学的一个分支或分支，研究生物有机体的结构和功能的复杂性，以及生物体和物种的出现和进化，重点研究生物网络内部和生物网络之间的复杂相互作用，以及对生命至关重要的基本关系和关系模式。因此，CSB 是一个理论科学领域，旨在发现和建模的关系模式的基本生命，只有一部分重叠的复杂系统理论，也与生物学的系统方法称为系统生物学; 这是因为后者主要限于简化模型的生物组织和有机体，以及只有一个哲学或语义问题的复杂性在生物学的一般考虑。此外，广泛的抽象理论复杂系统作为应用数学领域的研究，与生物学，化学或物理学有关或无关。
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，以及对生命至关重要的基本关系和关系模式。因此，CBS是一个理论科学领域，旨在发现和建模生命所必需的关系模式，它只与复杂系统理论有部分重叠，也与生物学的系统方法称为系统生物学;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，广泛的抽象理论复杂系统被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]]
[[File:Complex-adaptive-system.jpg|right|thumb|276px|Network Representation of a Complex Adaptive System]]
Network Representation of a Complex Adaptive System
Network Representation of a Complex Adaptive System
一个复杂自适应系统的网络表示
==Complexity of organisms and biosphere==
==Complexity of organisms and biosphere==
生物体和生物圈的复杂性
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. {{ISBN|978-0-8493-9712-7}}</ref>[[File:Seawifs global biosphere.jpg|thumb|right]]
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. {{ISBN|978-0-8493-9712-7}}</ref>[[File:Seawifs global biosphere.jpg|thumb|right]]
A complete definition of complexity for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.right
A complete definition of complexity for individual organisms, species, ecosystems, biological evolution and the biosphere has eluded researchers, and still is an ongoing issue.right
单个生物体、物种、生态系统、生物进化和生物圈的复杂性的完整定义一直困扰着研究人员，而且仍然是一个悬而未决的问题。
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. Recently, the two complementary approaches based both on information theory, network topology/abstract graph theory concepts are being combined for example in the fields of neuroscience and human cognition. 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. The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications
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. Recently, the two complementary approaches based both on information theory, network topology/abstract graph theory concepts are being combined for example in the fields of neuroscience and human cognition. 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. The hierarchy of complexity levels of organization in the biosphere is also recognized in modern classifications
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;然而，这些方法并不关注或不包括与组织、拓扑属性或代数拓扑有关的复杂性的概念部分，如非常重要的基因组、交互体和生物有机体的网络连通性。近年来，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领域得到了结合。人们普遍认为，组织的复杂程度存在一种层次结构，应与本体论的现实层次相区别。生物圈的复杂层次结构在现代分类等级分类中也得到承认，例如:生物领域和生物圈、生物界、门、生物纲、目、科、属和种。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”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简单动态系统的“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Autopoietic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也是一个新兴的领域，试图在CSB中填补棋盘自动机与更高层次复杂性方法之间的空白。
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: [[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
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)'',<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>
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><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.
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.
==Topics in complex systems biology==
==Topics in complex systems biology==
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]]]]
[[File:DNA animation.gif|thumb|right|100px|Animated Molecular Model of a DNA [[double helix]]]]
The following is only a partial list of topics covered in complex systems biology:
The following is only a partial list of topics covered in complex systems biology:
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
* [[Organism]]s and [[species]] relations and evolution
生物与物种的关系与进化
* [[Intertidal ecology|Interactions among Species]]
* [[Intertidal ecology|Interactions among Species]]
物种间的相互作用
* [[Evolution]] theories and [[population genetics]]
* [[Evolution]] theories and [[population genetics]]
进化论和种群遗传学
** [[Population genetics]] models
** [[Population genetics]] models
群体遗传学模型
** [[Epigenetics]]
** [[Epigenetics]]
表观遗传学
** [[Molecular evolution]] theories
** [[Molecular evolution]] theories
分子进化理论
* [[Quantum computer|Quantum biocomputation]]
* [[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>
* [[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<!-- Bot generated title -->]</ref>
* 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<!-- Bot generated title -->]</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)
* [[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]]
* [[Computational gene|Computational gene models]]
计算基因模型
** [[DNA topology]]
** [[DNA topology]]
DNA拓扑学
** [[DNA sequencing theory]]
** [[DNA sequencing theory]]
DNA测序理论
*[[Evolutionary developmental biology]]
*[[Evolutionary developmental biology]]
进化发育生物学
* [[Autopoiesis]]
* [[Autopoiesis]]
自我生产
* [[Protein folding]]
* [[Protein folding]]
蛋白质折叠
* [[Telomerase]] conformations and functions ''in vivo''
* [[Telomerase]] conformations and functions ''in vivo''
端粒酶在体内的构象和功能
* [[Epigenetics]]
* [[Epigenetics]]
表观遗传学
* [[Interactomics]]<ref name="Faith2007">
* [[Interactomics]]<ref name="Faith2007">
交互组学
{{cite journal
{{cite journal
* [[Cell signaling]]
* [[Cell signaling]]
细胞信号传导
* [[Signal transduction|Signal transduction networks]]
* [[Signal transduction|Signal transduction networks]]
信号转导网络
* [[neural net|Complex neural nets]]
* [[neural net|Complex neural nets]]
复杂神经网络
* [[Gene network|Genetic networks]]
* [[Gene network|Genetic networks]]
基因网络
* [[Morphogenesis]]
* [[Morphogenesis]]
形态发生
* [[Digital morphogenesis]]
* [[Digital morphogenesis]]
数字形态发生
* [[Complex adaptive system]]s
* [[Complex adaptive system]]s
复杂自适应系统
* [[Catastrophe theory|Topological models of morphogenesis]]
* [[Catastrophe theory|Topological models of morphogenesis]]
形态形成的拓扑模型
* [[Population dynamics of fisheries]]
* [[Population dynamics of fisheries]]
渔业种群动态
* [[Epidemiology]]
* [[Epidemiology]]
流行病学
* [[Theoretical ecology]]
* [[Theoretical ecology]]
理论生态学
* Immune system
* Immune system
免疫系统
==See also==
==See also==
另请参阅
{{Portal|Systems science|Mathematics}}
{{Portal|Systems science|Mathematics}}
*[[Mathematical and theoretical biology]]
*[[Mathematical and theoretical biology]]
数学和理论生物学
*[[Abstract relational biology]]
*[[Abstract relational biology]]
抽象关系生物学
* [[Complexity]]
* [[Complexity]]
复杂
*[[Complex system]]
*[[Complex system]]
复杂系统
*[[Biological system]]
*[[Biological system]]
生物系统
* [[Systems theory]]
* [[Systems theory]]
系统理论
* [[Dynamical system]]
* [[Dynamical system]]
动力系统
* [[Dynamical systems theory]]
* [[Dynamical systems theory]]
动力系统理论
* [[Automata theory]]
* [[Automata theory]]
自动机理论
* [[Cellular automaton]]
* [[Cellular automaton]]
元胞自动机
*[[Systems biology]]
*[[Systems biology]]
系统生物学
* [[Systems theory in anthropology]]
* [[Systems theory in anthropology]]
人类学中的系统理论
* [[Self-organization]]
* [[Self-organization]]
自组织
* [[Nonlinearity]]
* [[Nonlinearity]]
非线性度
* [[Generative sciences]]
* [[Generative sciences]]
生成科学
* [[Emergence]]
* [[Emergence]]
紧急情况
* [[Biosphere]]
* [[Biosphere]]
生物圈
*[[DNA]]
*[[DNA]]
脱氧核糖核酸
* [[Quantum biology]]
* [[Quantum biology]]
量子生物学
** [[Quantum mechanics|Quantum genetics]]
** [[Quantum mechanics|Quantum genetics]]
量子遗传学
** [[Quantum chemistry|Quantum biochemistry]]
** [[Quantum chemistry|Quantum biochemistry]]
量子生物化学
** [[Quantum chemistry]]
** [[Quantum chemistry]]
量子化学
** [[Molecular dynamics|Quantum molecular dynamics]]
** [[Molecular dynamics|Quantum molecular dynamics]]
量子分子动力学
*[[Protein folding]]
*[[Protein folding]]
蛋白质折叠
*[[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />
*[[Interactomics]]<ref name="Faith2007" /><ref name="Hayete2007" />
交互组学
*[[Genomics]]
*[[Genomics]]
基因组学
*[[Proteomics]]
*[[Proteomics]]
蛋白质组学
*[[Epigenetics]]
*[[Epigenetics]]
表观遗传学
* [[Digital morphogenesis]]
* [[Digital morphogenesis]]
数字形态
* [[Complex adaptive system]]
* [[Complex adaptive system]]
复杂自适应系统
* [[Multi-agent system]]s
* [[Multi-agent system]]s
多代理系统
* [[Cognitive Science]]
* [[Cognitive Science]]
认知科学
* [[Pattern oriented modeling]]
* [[Pattern oriented modeling]]
面向模式的建模
* [[Volatility, uncertainty, complexity and ambiguity]]
* [[Volatility, uncertainty, complexity and ambiguity]]
波动性，不确定性，复杂性和歧义性
* [[blue Gene]]
* [[blue Gene]]
蓝色基因
* [[Folding@home]]
* [[Folding@home]]
折叠@家
* [[Telomerase]]
* [[Telomerase]]
端粒酶
* [[What Is Life?]]
* [[What Is Life?]]
生命是什么
{{colend}}
{{colend}}
==Biographies==
==Biographies==
传记
{{colbegin}}
{{colbegin}}
==Notes==
==Notes==
笔记
{{Reflist|colwidth=30em}}
{{Reflist|colwidth=30em}}
===References cited===
===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 }}
* {{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 }}
==Further reading==
==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].
*[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].