Seleção natural falha na otimização de taxas de mutação para adaptação de longo termo em cenários de adaptação irregulares

sexta-feira, setembro 17, 2010

Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

Jeff Clune1,2*, Dusan Misevic3, Charles Ofria1, Richard E. Lenski4, Santiago F. Elena2,5, Rafael Sanjuán2,6

1 Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America, 2 Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientificas, Universidad Politecnica de València, València, Spain, 3 Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland, 4 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America, 5 The Santa Fe Institute, Santa Fe, New Mexico, United States of America, 6 Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, València, Spain


The 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.

Author Summary 

Natural selection is shortsighted and therefore does not necessarily drive populations toward improved long-term performance. Some traits may evolve because they provide immediate gains, even though they are less successful in the long run than some alternatives. Here, we use digital organisms to analyze the ability of evolving populations to optimize their mutation rate, a fundamental evolutionary parameter. We show that when the mutation rate is constrained to be high, populations adapt considerably faster over the long term than when the mutation rate is allowed to evolve. By varying the fitness landscape, we show that natural selection tends to reduce the mutation rate on rugged landscapes (but not on smooth ones) so as to avoid the production of harmful mutations, even though this short-term benefit limits adaptation over the long term.

Citation: Clune J, Misevic D, Ofria C, Lenski RE, Elena SF, et al. (2008) Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes. PLoS Comput Biol 4(9): e1000187. doi:10.1371/journal.pcbi.1000187

Editor: Laurence D. Hurst, University of Bath, United Kingdom

Received: April 21, 2008; Accepted: August 18, 2008; Published: September 26, 2008

Copyright: © 2008 Clune 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.

Funding: This 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 València was supported by grant BFU2006-14819-C02-01/BMC and the Ramón y Cajal program from the Spanish MEC.

Competing interests: The authors have declared that no competing interests exist.

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