Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes.

ABSTRACT: Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. H

SUBMITTER: Kuo YP 

PROVIDER: S-EPMC11151934 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature