Degeneracy (biology)

Within biological systems, degeneracy occurs when structurally dissimilar components/modules/pathways can perform similar functions (i.e. are effectively interchangeable) under certain conditions, but perform distinct functions in other conditions.[1][2] 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.[1][3]

Note that this use of the term has practically no relevance to the questionably meaningful concept of evolutionarily degenerate populations that have lost ancestral functions.

Biological examples

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),[4][5] protein complex assemblies,[6] ontogenesis,[7] the nervous system,[8] cell signalling (crosstalk) and numerous other biological contexts reviewed in.[1]

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.[3] 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.[9] 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.[10][11] 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.[12][13] Other examples from various levels of biological organization can be found in.[1]


Theoretical relationships between biological properties that are important to evolution. For a review of evidence that supports these relationships, see.[3]

Several theoretical developments have outlined links between degeneracy and important biological measurements related to robustness, complexity, and evolvability. These include:

See also


Further reading

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.

Animal Communication

Cultural Variation



History and philosophy of science

Systems biology



Artificial life, Computational intelligence




Peer Review


External links


  1. 1 2 3 4 Edelman and Gally; Gally, J. A. (2001). "Degeneracy and complexity in biological systems". Proceedings of the National Academy of Sciences, USA. 98 (24): 13763–13768. doi:10.1073/pnas.231499798.
  2. Mason, Paul H. (2 January 2015). "Degeneracy: Demystifying and destigmatizing a core concept in systems biology". Complexity. 20 (3): 12–21. doi:10.1002/cplx.21534.
  3. 1 2 3 4 5 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.
  4. Atamas (2005). "Les affinités électives". Pour la Science. 46: 39–43.
  5. 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 1461000Freely accessible. PMID 10757778.
  6. 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 2683819Freely accessible. PMID 19416527.
  7. Newman (1994). "Generic physical mechanisms of tissue morphogenesis: A common basis for development and evolution". Journal of Evolutionary Biology. 7 (4): 480. doi:10.1046/j.1420-9101.1994.7040467.x.
  8. 1 2 Tononi; Sporns, O.; Edelman, G. M.; 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.
  9. Guo; Styles, C. A.; Feng, Q.; Fink, G. R.; 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.
  10. Kitano (2004). "Biological robustness". Nature Reviews Genetics. 5 (11): 826–837. doi:10.1038/nrg1471. PMID 15520792.
  11. 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.
  12. Huang; Mukasa, A.; Bonavia, R.; Flynn, R. A.; Brewer, Z. E.; Cavenee, W. K.; Furnari, F. B.; White, F. M.; et al. (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.
  13. Stommel; 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.
  14. 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 2901314Freely accessible. PMID 20550663. Retrieved 2011-03-11.
  15. 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.
  16. 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. PMID 21840371.
  17. 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. PMID 21466836.
  18. 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. PMID 20932875.
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