Adaptation, fitness landscape learning and fast evolution [version 2; peer review: 2 approved]

We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on  fitness we prove that such model organisms  are capable, to some extent, to recognize the fitness landscap...

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Bibliographic Details
Main Authors: John Reinitz, Sergey Vakulenko, Dmitri Grigoriev, Andreas Weber
Format: Article
Language:English
Published: F1000 Research Ltd 2019-09-01
Series:F1000Research
Online Access:https://f1000research.com/articles/8-358/v2
Description
Summary:We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on  fitness we prove that such model organisms  are capable, to some extent, to recognize the fitness landscape. That fitness landscape learning sharply reduces the number of mutations needed for adaptation. Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype.  We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to  many constraints associated with a hard environment, if that environment becomes harder step by step. Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.
ISSN:2046-1402