复杂系统生物学

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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,[1] and on the fundamental relations and relational patterns that are essential to life.[2][3][4][5][6] 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,[7] 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] 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 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。

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

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.[3][8]

Seawifs global biosphere.jpg

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.[6][9][10] 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.[7][11] 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.[7][12][13] 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: 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[7][14] 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,;[15] it also differs from the definition proposed for example by K.D. Palmer in meta-system engineering,[16] organisms being quite different from machines and automata with fixed input-output transition functions, or a continuous dynamical system with fixed phase space,引用错误：没有找到与</ref>对应的<ref>标签 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),[17] 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[18][19] 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[20][21] 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

复杂系统生物学的主题

Animated Molecular Model of a DNA double helix

Animated Molecular Model of a DNA double helix

DNA 的动态分子模型双螺旋

Telomerase structure and function

Telomerase structure and function

端粒酶的结构与功能

A Complex Signal Transduction Pathway

A Complex Signal Transduction Pathway

一条复杂的信号转导

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: 

生物与物种的关系与进化

物种间的相互作用

进化论和种群遗传学

群体遗传学模型

表观遗传学

分子进化理论

量子生物计算学

量子遗传

关系生物学

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

DNA拓扑学

DNA测序理论

进化发育生物学

自我生产

蛋白质折叠

端粒酶在体内的构象和功能

表观遗传学

{{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 >

细胞信号传导

信号转导网络

复杂神经网络

基因网络

形态发生

数字形态发生

复杂自适应系统

形态形成的拓扑模型

渔业种群动态

流行病学

理论生态学

  • Immune system

免疫系统


See also

另请参阅 模板:Portal

模板:Biological classification

模板:Colbegin

数学和理论生物学

抽象关系生物学

复杂

复杂系统

生物系统

系统理论

动力系统

动力系统理论

自动机理论

元胞自动机

系统生物学

人类学中的系统理论

自组织

非线性度

生成科学

紧急情况

生物圈

脱氧核糖核酸

量子生物学

量子遗传学

量子生物化学

量子化学

量子分子动力学

蛋白质折叠

交互组学

基因组学

蛋白质组学

表观遗传学

数字形态

复杂自适应系统

多代理系统

认知科学

面向模式的建模

波动性，不确定性，复杂性和歧义性

蓝色基因

折叠@家

端粒酶

生命是什么


Biographies

传记 模板:Colbegin


Notes

笔记

  1. ↑ Sprites, P; Glymour, C; Scheines, R (2000). Causation, Prediction, and Search: Adaptive Computation and Machine Learning (2nd ed.). MIT Press. 
  2. ↑ Donald Snooks, Graeme, "A general theory of complex living systems: Exploring the demand side of dynamics", Complexity, vol. 13, no. 6, July/August 2008.
  3. ↑ 3.0 3.1 Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.
  4. ↑ 4.0 4.1 Rosen, R. (1958a). "A Relational Theory of Biological Systems". Bulletin of Mathematical Biophysics. 20 (3): 245–260. doi:10.1007/bf02478302.
  5. ↑ Baianu, I. C. (2006). "Robert Rosen's Work and Complex Systems Biology". Axiomathes. 16 (1–2): 25–34. doi:10.1007/s10516-005-4204-z.
  6. ↑ 6.0 6.1 Rosen, R. (1958b). "The Representation of Biological Systems from the Standpoint of the Theory of Categories". Bulletin of Mathematical Biophysics. 20 (4): 317–341. doi:10.1007/bf02477890.
  7. ↑ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.
  8. ↑ ^ Heylighen, Francis (2008). "Complexity and Self-Organization". In Bates, Marcia J.; Maack, Mary Niles. Encyclopedia of Library and Information Sciences. CRC.
  9. ↑ "abstract relational biology (ARB)". PlanetPhysics. Retrieved 2010-03-17.
  10. ↑ 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)
  11. ↑ Poli R (2001a). "The Basic Problem of the Theory of Levels of Reality". Axiomathes. 12 (3–4): 261–283. doi:10.1023/A:1015845217681.
  12. ↑ Poli R (1998). "Levels". Axiomathes. 9 (1–2): 197–211. doi:10.1007/bf02681712. PMID 8053082.
  13. ↑ Metasystem Transition Theory, Valentin Turchin, Cliff Joslyn, 1993-1997
  14. ↑ Reflexive Autopoietic Systems Theory
  15. ↑ Meta-system Engineering, Kent D. Palmer, 1996
  16. ↑ John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman.2000. Introduction to Automata Theory, Languages, and Computation (2nd Edition)Pearson Education.
  17. ↑ 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.)
  18. ↑ Framework for Systems with Dynamic Topology. International Journal of Computing Anticipatory System's 14:259–270
  19. ↑ 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.)
  20. ↑ Peak, West; Messinger, Mott; Messinger, SM; Mott, KA (2004). "Evidence for complex, collective dynamics and emergent, distributed computation in plants". Proceedings of the National Academy of Sciences of the USA. 101 (4): 918–922. Bibcode:2004PNAS..101..918P. doi:10.1073/pnas.0307811100. PMC 327117. PMID 14732685.
  21. ↑ Rosen, R. 1960. (1960). "A quantum-theoretic approach to genetic problems". Bulletin of Mathematical Biophysics. 22 (3): 227–255. doi:10.1007/BF02478347.
  22. ↑ Baianu, I. C.: 2006 (2006). "Robert Rosen's Work and Complex Systems Biology". Axiomathes. 16 (1–2): 25–34. doi:10.1007/s10516-005-4204-z.
  23. ↑ Rosen, R.: 1958b (1958). "The Representation of Biological Systems from the Standpoint of the Theory of Categories". Bulletin of Mathematical Biophysics. 20 (4): 317–341. doi:10.1007/BF02477890.
  24. ↑ PlanetMath
  25. ↑ "PlanetMath". PlanetMath. Retrieved 2010-03-17.
  26. ↑ 27.0 27.1 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. }}
  27. ↑ 28.0 28.1 28.2 }} 引用错误：无效<ref>标签；name属性“Hayete2007”使用不同内容定义了多次


References cited

参考文献引用

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