Evolutionary graph theory beyond pairwise interactions: Higher-order network motifs shape times to fixation in structured populations.

ABSTRACT: To design population topologies that can accelerate rates of solution discovery in directed evolution problems or for evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution, under elevated mutation rate. Here we find that moving beyond dyadic interactions in population graphs is fundamental to explain the trade-offs between probabilities and times to fixation of new mutants in the population. We show that higher

SUBMITTER: Kuo YP 

PROVIDER: S-EPMC10971782 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature