This award funds the research activities of Professor Michael Desai at Harvard University.
In the large and mostly asexual populations characteristic of many microbes and viruses, multiple mutations are often present simultaneously. Natural selection cannot act on each separately, but rather only on combinations of mutations linked together on physical chromosomes, an effect known as interference selection. Evolution in these populations involves a complex balance between the accumulation of beneficial mutations (adaptation) and the stochastic accumulation of weakly deleterious mutations (Muller's ratchet). In this project, the PI will develop mathematical models to predict how this interference selection among the full spectrum of beneficial and deleterious mutations alters evolutionary dynamics and genetic diversity in microbial populations, and test these predictions using experimental evolution of budding yeast as a model system. The project will focus on (1) the distribution of fitness within a population, (2) the distribution of fitness effects of fixed mutations, and (3) the within-population patterns of genetic diversity.
This research will also have significant broader impacts. It will involve graduate students and postdocs, providing interdisciplinary training involving the integration of methods from statistical physics, evolutionary biology, population genetics, and applied mathematics. The PI will also develop new course curricula to help physical scientists transition into research in evolution and population genetics. This will include an undergraduate course providing a mathematically sophisticated but biologically naïve introduction to the quantitative basis of evolutionary theory. In addition, the PI will co-organize a summer school in Cargese, France, to serve the broader community of physical scientists at the graduate and postdoctoral levels seeking to transition into evolution and population genetics. The summer school will use the research in this proposal as one of several case studies illustrating how population genetic theory, bioinformatics, and quantitative experiments can all contribute to our understanding of evolution.
