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dc.contributor.authorClune, Jeff-
dc.contributor.authorMisevic, Dusan-
dc.contributor.authorOfria, Charles-
dc.contributor.authorLenski, Richard E.-
dc.contributor.authorElena, Santiago F.-
dc.contributor.authorElena, Santiago F.-
dc.contributor.authorSanjuán, Rafael-
dc.date.accessioned2012-04-04T11:07:28Z-
dc.date.available2012-04-04T11:07:28Z-
dc.date.issued2008-09-26-
dc.identifier.citationPLoS Computational Biology 4/9:e1000187 (2008)es_ES
dc.identifier.urihttp://hdl.handle.net/10261/47932-
dc.description.abstractThe rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms.es_ES
dc.description.sponsorshipThis work was supported, in part, by the Defense Advanced Research Projects Agency ‘‘Fun Bio’’ Program, National Science Foundation grant CCF- 0643952, and the Cambridge Templeton Consortium. Work in Vale`ncia was supported by grant BFU2006-14819-C02-01/BMC and the Ramo´n y Cajal program from the Spanish MEC.es_ES
dc.language.isoenges_ES
dc.publisherPublic Library of Sciencees_ES
dc.relation.isversionofPublisher's version-
dc.rightsopenAccesses_ES
dc.subjectDNA Mismatch repaires_ES
dc.subjectDigital organismses_ES
dc.subjectReplication fidelityes_ES
dc.subjectAsexual populationses_ES
dc.titleNatural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapeses_ES
dc.typeartículoes_ES
dc.identifier.doi10.1371/journal.pcbi.1000187-
dc.description.peerreviewedPeer reviewedes_ES
dc.identifier.e-issn1553-734X-
dc.identifier.pmid18818724-
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.openairetypeartículo-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
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