Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

ABSTRACT: The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial C

SUBMITTER: Avecilla G 

PROVIDER: S-EPMC9140244 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature