||This article needs more links to other articles to help integrate it into the encyclopedia. (February 2013)|
Within biological systems, degeneracy refers to circumstances where structurally dissimilar components/modules/pathways can perform similar functions (i.e. are effectively interchangeable) under certain conditions, but perform distinct functions in other conditions. Degeneracy is thus a relational property that requires comparing the behaviour of two or more components. In particular, if degeneracy is present in a pair of components then there will exist conditions where the pair will appear functionally redundant but other conditions where they will appear functionally distinct.
Examples of degeneracy are found in the genetic code, when many different nucleotide sequences encode the same polypeptide; in protein folding, when different polypeptides fold to be structurally and functionally equivalent; in protein functions, when overlapping binding functions and similar catalytic specificities are observed; in metabolism, when multiple, parallel biosynthetic and catabolic pathways may coexist. More generally, degeneracy is observed in proteins of every functional class (e.g. enzymatic, structural, or regulatory), protein complex assemblies, ontogenesis (see page 14 in), the nervous system, cell signalling (crosstalk) and numerous other biological contexts reviewed in.
Contribution to Robustness
Degeneracy contributes to the robustness of biological traits through several mechanisms. Degenerate components compensate for one another under conditions where they are functionally redundant, thus providing robustness against component or pathway failure. Because degenerate components are somewhat different, they tend to harbour unique sensitivities so that a targeted attack such as a specific inhibitor is less likely to present a risk to all components at once. There are numerous biological examples where degeneracy contributes to robustness in this way. For instance, gene families can encode for diverse proteins with many distinctive roles yet sometimes these proteins can compensate for each other during lost or suppressed gene expression, as seen in the developmental roles of the adhesins gene family in Saccharomyces. Nutrients can be metabolized by distinct metabolic pathways that are effectively interchangeable for certain metabolites even though the total effects of each pathway are not identical. In cancer, therapies targeting the EGF receptor are thwarted by the co-activation of alternate receptor tyrosine kinases (RTK) that have partial functional overlap with the EGF receptor (and are therefore degenerate), but are not targeted by the same specific EGF receptor inhibitor. Other examples from various levels of biological organization can be found in.
Several theoretical developments have outlined links between degeneracy and important biological measurements related to robustness, complexity, and evolvability. These include:
- Theoretical arguments supported by simulations have proposed that degeneracy can lead to distributed forms of robustness in protein interaction networks. Those authors suggest that similar phenomena is likely to arise in other biological networks and potentially may contribute to the resilience of ecosystems as well.
- Tononi et al. have found evidence that degeneracy is inseparable from the existence of hierarchical complexity in neural populations. They argue that the link between degeneracy and complexity is likely to be much more general.
- Fairly abstract simulations have supported the hypothesis that degeneracy fundamentally alters the propensity for a genetic system to access novel heritable phenotypes and that degeneracy could therefore be a precondition for open-ended evolution.
- The three hypotheses above have been integrated in where they propose that degeneracy plays a central role in the open-ended evolution of biological complexity. In the same article, it was argued that the absence of degeneracy within many designed (abiotic) complex systems may help to explain why robustness appears to be in conflict with flexibility and adaptability, as seen in software, systems engineering, and artificial life.
Because there are many distinct types of systems that undergo heritable variation and selection (see Universal Darwinism), degeneracy has become a highly interdisciplinary topic. The following provides a brief roadmap to the application and study of degeneracy within different disciplines.
- Atamas, S. and J. Bell, Degeneracy-Driven Self-Structuring Dynamics in Selective Repertoires. Bulletin of Mathematical Biology, 2009. 71(6) p. 1349-1365.
History and Philosophy
- Mason, P.H., Degeneracy at Multiple Levels of Complexity. Biological Theory, 2010. 5(3) p. 277-288.
- Solé, R.V., R. Ferrer-Cancho, J.M. Montoya, and S. Valverde, Selection, tinkering, and emergence in complex networks. Complexity, 2002. 8(1) p. 20-33.
- Whitacre, J.M. and A. Bender, Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems. Theoretical Biology and Medical Modelling 2010. 7(20).
- Edelman, G.M. and J.A. Gally, Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, USA, 2001. 98(24) p. 13763-13768.
- Whitacre, J.M., Degeneracy: a link between evolvability, robustness and complexity in biological systems. Theoretical Biology and Medical Modelling, 2010. 7(6).
- Whitacre, J.M. and A. Bender, Degeneracy: a design principle for achieving robustness and evolvability. Journal of Theoretical Biology, 2010. 263(1) p. 143-53.
- Whitacre, J.M. and S.P. Atamas, The Diversity Paradox: How Nature Resolves an Evolutionary Dilemma. Biology Direct, (in press).
- Cohn, M., Degeneracy, mimicry and crossreactivity in immune recognition. Molecular immunology, 2005. 42(5) p. 651-655.
- Cohen, I.R., U. Hershberg, and S. Solomon, Antigen-receptor degeneracy and immunological paradigms. Molecular immunology, 2004. 40(14-15) p. 993-996.
- Tieri, P., G.C. Castellani, D. Remondini, S. Valensin, J. Loroni, S. Salvioli, and C. Franceschi, Capturing degeneracy of the immune system. In Silico Immunology. Springer, 2007.
- Tieri, P., A. Grignolio, A. Zaikin, M. Mishto, D. Remondini, G.C. Castellani, and C. Franceschi, Network, degeneracy and bow tie integrating paradigms and architectures to grasp the complexity of the immune system. Theor Biol Med Model, 2010. 7: p. 32.
- Andrews, P.S. and J. Timmis, A Computational Model of Degeneracy in a Lymph Node. Lecture Notes in Computer Science, 2006. 4163: p. 164.
- Mendao, M., J. Timmis, P.S. Andrews, and M. Davies. The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System. in Foundations of Computational Intelligence (FOCI). 2007.
- Whitacre, J.M. and A. Bender. Degenerate neutrality creates evolvable fitness landscapes. in WorldComp-2009. 2009. Las Vegas, Nevada, USA.
- Whitacre, J.M., P. Rohlfshagen, X. Yao, and A. Bender. The role of degenerate robustness in the evolvability of multi-agent systems in dynamic environments. in PPSN XI. 2010. Kraków, Poland.
- Macia, J. and R. Solé, Distributed robustness in cellular networks: insights from synthetic evolved circuits. Royal Society Interface, 2009 6: p. 393-400.
- Fernandez-Leon, J.A. (2011). Evolving cognitive-behavioural dependencies in situated agents for behavioural robustness. BioSystems 106, pp. 94–110.
- Fernandez-Leon, J.A. (2011). Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics. BioSystems 105, Elsevier, pp. 49–61.
- Fernandez-Leon, J.A. (2010). Evolving experience-dependent robust behaviour in embodied agents. BioSystems 103:1, Elsevier, pp. 45–56.
- Price, C. and K. Friston, Degeneracy and cognitive anatomy. Trends in Cognitive Sciences, 2002. 6(10) p. 416-421.
- Tononi, G., O. Sporns, and G.M. Edelman, Measures of degeneracy and redundancy in biological networks. Proceedings of the National Academy of Sciences, USA, 1999. 96(6) p. 3257-3262.
- Tian, T., S. Olson, J.M. Whitacre, and A. Harding, The origins of cancer robustness and evolvability. Integrative Biology, 2011. 3: p. 17-30.
- Lehky, S., Peer Evaluation and Selection Systems: Adaptation and Maladaptation of Individuals and Groups through Peer Review. 2011: BioBitField Press.
- Edelman and Gally (2001). "Degeneracy and complexity in biological systems". Proceedings of the National Academy of Sciences, USA 98 (24): 13763–13768. doi:10.1073/pnas.231499798.
- Whitacre (2010). "Degeneracy: a link between evolvability, robustness and complexity in biological systems". Theoretical Biology and Medical Modelling 7 (6): 6. doi:10.1186/1742-4682-7-6. Retrieved 2011-03-11.
- Atamas (2005). "Les affinités électives". Pour la Science 46: 39–43.
- Wagner (2000). "The role of population size, pleiotropy and fitness effects of mutations in the evolution of overlapping gene functions". Genetics 154 (3): 1389–1401. PMC 1461000. PMID 10757778.
- Kurakin (2009). "Scale-free flow of life: on the biology, economics, and physics of the cell". Theoretical Biology and Medical Modelling 6 (1): 6. doi:10.1186/1742-4682-6-6. PMC 2683819. PMID 19416527.
- Newman (1994). "Generic physical mechanisms of tissue morphogenesis: A common basis for development and evolution". Journal of Evolutionary Biology 7 (4): 467–488. doi:10.1046/j.1420-9101.1994.7040467.x.
- Tononi et al. (1999). "Measures of degeneracy and redundancy in biological networks". Proceedings of the National Academy of Sciences, USA 96 (6): 3257–3262. doi:10.1073/pnas.96.6.3257.
- Guo et al. (2000). "A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating". Proceedings of the National Academy of Sciences, USA 97 (22): 12158–12163. doi:10.1073/pnas.220420397.
- Kitano (2004). "Biological robustness". Nature Reviews Genetics 5 (11): 826–837. doi:10.1038/nrg1471. PMID 15520792.
- Ma and Zeng; Zeng, AP (2003). "The connectivity structure, giant strong component and centrality of metabolic networks". Bioinformatics 19 (11): 1423–1430. doi:10.1093/bioinformatics/btg177. PMID 12874056.
- Huang et al.; Mukasa, A.; Bonavia, R.; Flynn, R. A.; Brewer, Z. E.; Cavenee, W. K.; Furnari, F. B.; White, F. M. (2007). "Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma". Proceedings of the National Academy of Sciences 104 (31): 12867. doi:10.1073/pnas.0705158104.
- Stommel et al.; Kimmelman, AC; Ying, H; Nabioullin, R; Ponugoti, AH; Wiedemeyer, R; Stegh, AH; Bradner, JE et al. (2007). "Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies". Science 318 (5848): 287–90. doi:10.1126/science.1142946. PMID 17872411.
- Whitacre and Bender; Bender, Axel (2010). "Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems". Theoretical Biology and Medical Modelling 7 (20): 20. doi:10.1186/1742-4682-7-20. PMC 2901314. PMID 20550663. Retrieved 2011-03-11.
- Whitacre and Bender; Bender, A (2010). "Degeneracy: a design principle for achieving robustness and evolvability". Journal of Theoretical Biology 263 (1): 143–153. doi:10.1016/j.jtbi.2009.11.008. PMID 19925810. Retrieved 2011-03-11.
- Fernandez-Leon, J.A. (2011). "Evolving cognitive-behavioural dependencies in situated agents for behavioural robustness". BioSystems 106 (2-3): 94–110. doi:10.1016/j.biosystems.2011.07.003.
- Fernandez-Leon, J.A. (2011). "Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics". BioSystems 105 (1): 49–61. doi:10.1016/j.biosystems.2011.03.006.
- Fernandez-Leon, J.A. (2010). "Evolving experience-dependent robust behaviour in embodied agents". BioSystems 103 (1): 45–56. doi:10.1016/j.biosystems.2010.09.010.