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      Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest.

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          Abstract

          Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit genotypes (with high fitness but low robustness)-a phenomenon known as survival of the flattest. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of fit and flat genotypes has been lacking, leading to uncertainty about the genetic basis of the survival-of-the-flattest effect. Here, we repeated the survival-of-the-flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that the flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

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          Author and article information

          Journal
          Artif Life
          Artificial life
          MIT Press - Journals
          1530-9185
          1064-5462
          2019
          : 25
          : 3
          Affiliations
          [1 ] Michigan State University, Department of Microbiology and Molecular Genetics.
          [2 ] Harvard University, Department of Molecular and Cellular Biology. thomas_labar@fas.harvard.edu.
          [3 ] Michigan State University, BEACON Center for the Study of Evolution in Action.
          [4 ] Michigan State University, Department of Microbiology and Molecular Genetics; Department of Ecology, Evolutionary Biology, and Behavior; BEACON Center for the Study of Evolution in Action.
          [5 ] Arizona State University, Department of Physics and Astronomy.
          Article
          10.1162/artl_a_00296
          31397601
          752d0f43-6b20-4197-95e4-9a2a6188d460
          History

          Digital organisms,adaptation,evolution,mutational robustness

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