Accelerated simulation of evolutionary trajectories in origin-fixation models.

ABSTRACT: We present an accelerated algorithm to forward-simulate origin-fixation models. Our algorithm requires, on average, only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations of the order of the effective population size, N_e Our accelerated algorithm yields the exact same steady state as the original algorithm but produces a different order of fixed mutations. By comparing several relevant evolutionary metrics, such as the distribution of fixed selection coefficients and the probability of reversion, we find that the two algorithms behave equivalently in many respects. However, the accelerated algorithm yields less variance in fixed selection coefficients. Notably, we are able to r

SUBMITTER: Teufel AI 

PROVIDER: S-EPMC5332577 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature