Molecular Evolution in Rapidly Evolving Populations

Advances in DNA sequencing are creating new opportunities for studying the process of evolution. These measurements can be particularly useful for rapidly evolving microbial organisms, whose small size and fast generation times make them ideal for controlled laboratory experiments and for tracking replicate populations in vivo. However, the interpretation of this new source of data is complicated by the unique ways in which these large microbial populations evolve. The basic problem is that natural selection is forced to do too many things at once. Unlike the classical picture, where new mutations arise one-by-one, rapidly evolving populations often harbor many selected variants at the same time. When recombination is limited, selection cannot act on these mutations individually, but only on combinations of mutations that happen to arise on the same genetic background. These effects, known as clonal interference, create correlations along the genome that are difficult to disentangle. Existing population genetic models often neglect these effects, which leaves us at loss when interpreting data from these populations. In Chapters 2-5, we analyze the effects of clonal interference in a simple ``null model'' of microbial evolution. We focus on the simplest model that is consistent with two empirical observations: (1) many fitness-influencing mutations are created every generation and (2) mutations have a broad range of fitness effects. After analyzing the basic dynamics of this model, we obtain predictions for the substitution rates of individual mutations and the patterns of linked neutral diversity, and we show how these quantities depend on the population size, mutation and recombination rates, and the fitness effects of new mutations. In Chapters 6 and 7, we apply this null model to data from laboratory experiments in S. cerevisiae and E. coli. We develop a statistical framework to infer the underlying parameters (the fitness effects of new mutations), which allows us to quantify deviations from the model over longer evolutionary timescales. Finally, in Chapters 8 and 9, we investigate the behavior of the model when some of the parameters are allowed to evolve or change in time.Physic