Abstracts (first author)
Epistatic constraints and evolutionary predictability on empirical fitness landscapes
The adaptive dynamics of an asexual population in the space of genotypes is constrained by epistatic interactions between mutations at different genetic loci. Recent empirical studies have shown that this strongly reduces the number of mutational pathways that are accessible under conditions of strong selection and weak mutation (SSWM). In the talk I will describe statistical models for fitness landscapes that quantify evolutionary accessibility under different assumptions on the amount of epistasis as well as on the underlying genetic architecture, and show how these models can be used to classify and interpret empirical data sets. I then discuss the impact of epistatic constraints on the predictability of evolutionary trajectories in asexuals, with particular emphasis on the role of population size. With increasing population size clonal interference implies a preference for mutational steps of large effect, which leads to an increase in predictability beyond the expectation under SSWM dynamics. However, a further increase of population size reduces predictability by opening up new pathways that involve the crossing of fitness valleys by multiple mutations. This nonmonotonic pattern of evolutionary predictability is found in large-scale simulations on an empirical fitness landscape, and argued to be observable in experiments that monitor the variability of fitness trajectories among replicate populations.