Abstracts Mathematics

Some problems in probabilistic modeling of germline and somatic evolutionary processes

by William S. DeWitt

Evolutionary processes shape biological systems at all scales, and understanding evolutionary mechanisms requires quantitative frameworks that are matched in sophistication to modern experimental capabilities. This dissertation covers quantitative work along two biological threads – evolutionary genomics and adaptive immunology. I describe how complex dynamics of mutational activity in evolving populations can be recovered from population-level whole-genome sequencing data, and show results on mutation spectrum evolution over thousands of generations in humans. Next, I describe inference of evolutionary histories in a regime of dense single-cell sampling of cellular diversification, where identical genotypes from clonal subpopulations are sampled, and genotype abundance influences the mutational output of a clone because it is closely related to clonal population size. In particular, I address phylogenetic tree inference for B cells evolving improved antibodies. I conclude with an outlook for future research that synthesizes evolutionary genomics and adaptive immunology, and views the latter as a powerful evolutionary model system.