Multiple Adaptive Substitutions During Evolution in Novel Environments
Author(s) -
Kavita Jain,
Sarada Seetharaman
Publication year - 2011
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1534/genetics.111.134163
Subject(s) - biology , fitness landscape , genetic fitness , population , selection (genetic algorithm) , evolutionary biology , fitness approximation , mutation , adaptation (eye) , exponential growth , fitness proportionate selection , genetics , population size , adaptive evolution , fitness function , biological evolution , mathematics , mathematical optimization , gene , demography , computer science , genetic algorithm , mathematical analysis , artificial intelligence , neuroscience , sociology
We consider an asexual population under strong selection-weak mutation conditions evolving on rugged fitness landscapes with many local fitness peaks. Unlike the previous studies in which the initial fitness of the population is assumed to be high, here we start the adaptation process with a low fitness corresponding to a population in a stressful novel environment. For generic fitness distributions, using an analytic argument we find that the average number of steps to a local optimum varies logarithmically with the genotype sequence length and increases as the correlations among genotypic fitnesses increase. When the fitnesses are exponentially or uniformly distributed, using an evolution equation for the distribution of population fitness, we analytically calculate the fitness distribution of fixed beneficial mutations and the walk length distribution.
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