EFFECTS OF POPULATION SIZE AND MUTATION RATE ON THE EVOLUTION OF MUTATIONAL ROBUSTNESS
Abstract
It is often assumed that the efficiency of selection for mutational robustness would be proportional to mutation rate and population size, thus being inefficient in small populations. However, Krakauer and Plotkin (2002) hypothesized that selection in small populations would favor robustness mechanisms, such as redundancy, that mask the effect of deleterious mutations. In large populations, by contrast, selection is more effective at removing deleterious mutants and fitness would be improved by eliminating mechanisms that mask the effect of deleterious mutations and thus impede their removal. Here, we test whether these predictions are supported in experiments with evolving populations of digital organisms. Digital organisms are self-replicating programs that inhabit a virtual world inside a computer. Like their organic counterparts, digital organisms mutate, compete, evolve, and adapt by natural selection to their environment. In this study, 160 populations evolved at different combinations of mutation rate and population size. After 10(4) generations, we measured the mutational robustness of the most abundant genotype in each population. Mutational robustness tended to increase with mutation rate and to decline with population size, although the dependence with population size was in part mediated by a negative relationship between fitness and robustness. These results are independent of whether genomes were constrained to their original length or allowed to change in size.
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The fitness increase is usually fast within the first 100 to 500 generations and slows considerably down during the course of ALE.
“Whereas the fitness increase is usually fast within the first 100 to 500 generations, it slows considerably down during the course of ALE [36].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [36]
WGS of parallel E. coli populations revealed that most cultures acquired mutations in the transcriptional regulator nadR, the pyruvate kinase pykF, the rbs operon, the transcriptional regulator malT and the stringent response regulator spoT.
“WGS of the parallel populations revealed that most cultures acquired mutations in the transcriptional regulator nadR, the pyruvate kinase pykF, the rbs operon, the transcriptional regulator malT and the stringent response regulator spoT[36].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [36]
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Adaptive laboratory evolution—Principles and applications for biotechnology
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- Authors
- Elena, S. F., Wilke, C. O., Ofria, C., & Lenski, R. E.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- The University of Texas at Austin (US)
- Michigan State University (US)
- Venue
- Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-03
- Volume
- 61
- Issue
- 3
- Pages
- 666-674
- ISSN
- 0014-3820 (ISSN-L)
- 1558-5646
- OpenAlex
- W2087004016
- Cited by
- 73
- References
- 55
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 2 · 2022: 5 · 2021: 1 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 2 · 2016: 4 · 2015: 3 · 2014: 7 · 2013: 6 · 2012: 5
- FWCI
- 2.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evolution (publishedVersion)
- PubMed (publishedVersion)
- BioOne Complete (BioOne) (submittedVersion)
- Funders
- National Science Foundation
- European Molecular Biology Organization
- Defense Advanced Research Projects Agency
- European Regional Development Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- mutational robustness, mutation rate, population size, digital organisms, deleterious mutations, redundancy, selection efficiency, genome size
- MeSH terms
- Animals, Computer Simulation, Genotype, Models, Biological, Mutation, Population Density, Selection, Genetic, Genome, Evolution, Molecular
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