Complex systems biology

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. 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.

Network Representation of a Complex Adaptive System

Complexity of organisms and biosphere

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]

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 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,[17] 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),[18] 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[19][20][21] 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
Animated Molecular Model of a DNA double helix
Telomerase structure and function
A Complex Signal Transduction Pathway

The following is only a partial list of topics covered in complex systems biology:

gollark: It should at least be possible to find actual real-world data on whether it's growing or shrinking.
gollark: The real lesson is "random pithy quotes are meaningless and should not be used for decision making or anything similar".
gollark: It seems like one of those very general quotes which can apply to a ton of situations and will probably produce inaccurate results in most of them.
gollark: Troubling.
gollark: Did someone purge this or is my client just being evil?

See also
LifeDomainKingdomPhylumClassOrderFamilyGenusSpecies
The hierarchy of biological classification's eight major taxonomic ranks. Intermediate minor rankings are not shown.

Biographies

Notes
  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. Bonner, J. T. 1988. The Evolution of Complexity by Means of Natural Selection. Princeton: Princeton University Press.
  4. 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. 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. ISBN 978-0-8493-9712-7
  8. ^ 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
  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. 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
  17. 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
  18. 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.)
  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. Faith, JJ; et al. (2007). "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles". PLOS Biology. 5 (1): 54–66. doi:10.1371/journal.pbio.0050008. PMC 1764438. PMID 17214507.
  27. Hayete, B; Gardner, TS; Collins, JJ (2007). "Size matters: network inference tackles the genome scale". Molecular Systems Biology. 3 (1): 77. doi:10.1038/msb4100118. PMC 1828748. PMID 17299414.

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  • 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.
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Further reading
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