A model for understanding long-term evolution experiments

Laboratory evolution experiments have proven to be a productive way of studying and gaining insights into evolutionary processes. Nevertheless, the dynamics of fitness over long time scales is still not well understood. Data from the Lenski lab’s long-term evolution experiment shows that the relative growth rates of bacteria continue to increase even after almost 30 years (over 65k generations). Despite the rate of fitness increase slowing down dramatically, mutations continue to accumulate at an almost constant rate. This type of adaptation dynamics has often been discussed in the context of fitness landscapes, where growth rates of mutants are assumed to be drawn from some distribution. In such a formalism, the concept of epistasis lies in the idea that the distribution of potential fitness effects varies with the current fitness. However, it is unclear if landscapes are indeed fitness-parametrized, since genotypes with the same fitness could potentially differ in their capacity to gain beneficial mutations that fix in a population, and how any proposed form of epistasis encoded in mutant fitness distributions could arise mechanistically. In this work, we compare various models for adaptation and present an alternative model to explain the observations in Lenski’s experiment.