Variable mutation rates as an adaptive strategy in replicator populations

Stich, Michael, Manrubia, Susanna C. and Lázaro, Ester (2010). Variable mutation rates as an adaptive strategy in replicator populations. PLoS ONE, 5 (6),

Abstract

For evolving populations of replicators, there is much evidence that the effect of mutations on fitness depends on the degree of adaptation to the selective pressures at play. In optimized populations, most mutations have deleterious effects, such that low mutation rates are favoured. In contrast to this, in populations thriving in changing environments a larger fraction of mutations have beneficial effects, providing the diversity necessary to adapt to new conditions. What is more, non-adapted populations occasionally benefit from an increase in the mutation rate. Therefore, there is no optimal universal value of the mutation rate and species attempt to adjust it to their momentary adaptive needs. In this work we have used stationary populations of RNA molecules evolving in silico to investigate the relationship between the degree of adaptation of an optimized population and the value of the mutation rate promoting maximal adaptation in a short time to a new selective pressure. Our results show that this value can significantly differ from the optimal value at mutation-selection equilibrium, being strongly influenced by the structure of the population when the adaptive process begins. In the short-term, highly optimized populations containing little variability respond better to environmental changes upon an increase of the mutation rate, whereas populations with a lower degree of optimization but higher variability benefit from reducing the mutation rate to adapt rapidly. These findings show a good agreement with the behaviour exhibited by actual organisms that replicate their genomes under broadly different mutation rates.

Publication DOI: https://doi.org/10.1371/journal.pone.0011186
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © 2010 Stich et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords: Agricultural and Biological Sciences(all),Biochemistry, Genetics and Molecular Biology(all),Medicine(all)
Full Text Link: http://journals ... al.pone.0011186
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2010
Authors: Stich, Michael ( 0000-0001-8862-1044)
Manrubia, Susanna C.
Lázaro, Ester

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