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

@article{citeulike:3352727, abstract = {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 = {Clune, Jeff and Misevic, Dusan and Ofria, Charles and Lenski, Richard E. and Elena, Santiago F. and Sanju\'{a}n, Rafael}, citeulike-article-id = {3352727}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000187}, day = {26}, doi = {10.1371/journal.pcbi.1000187}, journal = {PLoS Comput Biol}, keywords = {conceptual, evolution, fitness, landscapes, model, selection}, month = {September}, number = {9}, pages = {e1000187+}, posted-at = {2009-10-17 16:55:09}, priority = {2}, publisher = {Public Library of Science}, title = {Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000187}, volume = {4}, year = {2008} }

TY - JOUR ID - citeulike:3352727 L3 - citeulike-article-id:3352727 N2 - 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. IS - 9 JF - PLoS Comput Biol TI - Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes PB - Public Library of Science VL - 4 SP - e1000187 KW - conceptual KW - evolution KW - fitness KW - landscapes KW - model KW - selection AU - Clune, Jeff AU - Misevic, Dusan AU - Ofria, Charles AU - Lenski, Richard AU - Elena, Santiago AU - Sanjuán, Rafael PY - 2008/09/26/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000187 ER -