Speed of evolution in large asexual populations with diminishing returns

TL;DR

This paper introduces a phenomenological model for the evolution of large asexual populations that incorporates diminishing returns epistasis, analyzing its effects on adaptation speed and mutation rates through analytical and simulation methods.

Contribution

It extends existing models by including diminishing returns epistasis, providing a more realistic framework for understanding adaptation in large populations.

Findings

Model predicts slower adaptation with diminishing returns

Parameters can be fitted to experimental data

Mutation rate estimates align with previous measurements

Abstract

The adaptive evolution of large asexual populations is generally characterized by competition between clones carrying different beneficial mutations. This interference phenomenon slows down the adaptation speed and makes the theoretical description of the dynamics more complex with respect to the successional occurrence and fixation of beneficial mutations typical of small populations. A simplified modeling framework considering multiple beneficial mutations with equal and constant fitness advantage captures some of the essential features of the actual complex dynamics, and some key predictions from this model are verified in laboratory evolution experiments. However, in these experiments the relative advantage of a beneficial mutation is generally dependent on the genetic background. In particular, the general pattern is that, as mutations in different loci accumulate, the relative advantage of new mutations decreases, trend often referred to as "diminishing return" epistasis. In this paper, we propose a phenomenological model that generalizes the fixed-advantage framework to include in a simple way this feature. To evaluate the quantitative consequences of diminishing returns on the evolutionary dynamics, we approach the model analytically as well as with direct simulations. Finally, we show how the model parameters can be matched with data from evolutionary experiments in order to infer the mean effect of epistasis and derive order-of-magnitude estimates of the rate of beneficial mutations. Applying this procedure to two experimental data sets gives values of the beneficial mutation rate within the range of previous measurements.