Extensive DNA sequencing has revealed a tremendous amount of genetic diversity within species, including fine-scale diversity within local populations of single bacterial species in nature, as well as in populations evolved from a single clone in the laboratory. Yet the causes and statistical properties of this diversity are far from understood, with the evolutionary processes of mutation, recombination, selection, stochastic fluctuations, and spatial dispersal all playing roles, as well as the ecological interactions between subtypes and changing environments. The complexities of the organisms and environments mean that the eco-evo dynamics takes place in high-dimensional space. The primary goal of this project is to study various aspects of the evolutionary and ecological dynamics focussing on the statistical properties of the diversity, including aspects not previously studied, that these give rise to. This will be done by developing and analyzing simple models, some rough approximations of the actual processes and others abstract caricatures that endeavor to make use of the high-dimensional aspects to extract general features. Laboratory experiments and data from natural microbial communities, some of which the PI has been and will continue to be involved with, will motivate some of the modeling and the predictions will be compared with sequencing and other data. The theory to be developed on the statistical consequences of genetic hitchhiking on selection elsewhere in the genome has the potential to enable better use of human genetic diversity data to infer evolutionary history, and other aspects, of human populations. The educational impacts include development of courses and curricula bringing together biology and the quantitative sciences as well as training of women students and postdocs.
Better theory on the interplay between various sources of diversity and their observable consequences will advance both qualitative and quantitative understanding of short term microbial evolution. The project will focus on identifying statistical properties of genetic diversity that can most effectively distinguish between scenarios for evolutionary history and ecology when the phenotypic differences are subtle enough that they cannot be readily observed. This will enable more effective use of DNA sequencing data and motivate which types of data would be most informative for future studies. Likewise, it will will help motivate which laboratory experiments that combine evolutionary and ecological aspects experiments might shed most light on these complex processes. The modeling and analysis will involve developing some new statistical dynamical and asymptotic methods, as well as exploring the potential of crude caricature models to explore scenarios and develop intuition.
