Degeneracy: A design principle for achieving robustness and evolvability
Open accessAbstract
Robustness, the insensitivity of some of a biological system's functionalities to a set of distinct conditions, is intimately linked to fitness. Recent studies suggest that it may also play a vital role in enabling the evolution of species. Increasing robustness, so is proposed, can lead to the emergence of evolvability if evolution proceeds over a neutral network that extends far throughout the fitness landscape. Here, we show that the design principles used to achieve robustness dramatically influence whether robustness leads to evolvability. In simulation experiments, we find that purely redundant systems have remarkably low evolvability while degenerate, i.e. partially redundant, systems tend to be orders of magnitude more evolvable. Surprisingly, the magnitude of observed variation in evolvability can neither be explained by differences in the size nor the topology of the neutral networks. This suggests that degeneracy, a ubiquitous characteristic in biological systems, may be an important enabler of natural evolution. More generally, our study provides valuable new clues about the origin of innovations in complex adaptive systems.
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A recent study in S. cerevisiae indicated that anticipatory gene regulatory patterns can evolve under cycling salt and oxidative stress conditions on a laboratory time scale within 300 generations, and that cross-stress protection against salt and oxidative stress is strikingly asymmetrical, with oxidative stress protecting against salt stress but not vice versa.
“In fact, a recent study in S. cerevisiae indicated that anticipatory gene regulatory patterns can evolve under cycling salt and oxidative stress conditions on a laboratory time scale within 300 generations and that cross-stress protection against salt and oxidative stress is strikingly asymmetrical, with oxidative stress protecting against salt stress but not vice versa[120].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [120]
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- Authors
- Whitacre, J., & Bender, A.
- Affiliations
- UNSW Sydney (AU)
- Defence Science and Technology Group (AU)
- Corresponding author
- Whitacre, James
- ORCID
- Bender, Axel
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2010-03
- Volume
- 263
- Issue
- 1
- Pages
- 143-153
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W2055308071
- Cited by
- 197
- References
- 33
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 9 · 2023: 5 · 2022: 10 · 2021: 12 · 2020: 15 · 2019: 10 · 2018: 18 · 2017: 8 · 2016: 16 · 2015: 11 · 2014: 8 · 2013: 20 · 2012: 14
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- degeneracy, robustness, evolvability, neutral networks, fitness landscape, redundancy, design principles, complex adaptive systems, evolution
- MeSH terms
- Algorithms, Animals, Computer Simulation, Genetics, Population, Genotype, Humans, Models, Biological, Models, Genetic, Phenotype, Selection, Genetic, Software, Models, Statistical, Neural Networks, Computer, Evolution, Molecular, Gene Regulatory Networks
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