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The Stochastic Edge in Adaptive Evolution
Author(s) -
Éric Brunet,
Igor M. Rouzine,
Claus O. Wilke
Publication year - 2008
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.107.079319
Subject(s) - extrapolation , population , exponential function , statistical physics , enhanced data rates for gsm evolution , class (philosophy) , exponential growth , stochastic process , stochastic modelling , mathematics , biology , statistics , computer science , mathematical analysis , physics , artificial intelligence , demography , sociology
In a recent article, Desai and Fisher proposed that the speed of adaptation in an asexual population is determined by the dynamics of the stochastic edge of the population, that is, by the emergence and subsequent establishment of rare mutants that exceed the fitness of all sequences currently present in the population. Desai and Fisher perform an elaborate stochastic calculation of the mean time tau until a new class of mutants has been established and interpret 1/tau as the speed of adaptation. As they note, however, their calculations are valid only for moderate speeds. This limitation arises from their method to determine tau: Desai and Fisher back extrapolate the value of tau from the best-fit class's exponential growth at infinite time. This approach is not valid when the population adapts rapidly, because in this case the best-fit class grows nonexponentially during the relevant time interval. Here, we substantially extend Desai and Fisher's analysis of the stochastic edge. We show that we can apply Desai and Fisher's method to high speeds by either exponentially back extrapolating from finite time or using a nonexponential back extrapolation. Our results are compatible with predictions made using a different analytical approach (Rouzine et al.) and agree well with numerical simulations.

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