Interactions between Phenotypic Switching, Gene Network Dynamics and Evolutionary Dynamics in Growing Cell Populations

Gene expression is a stochastic biological processes that controls the different phenotypes of an organism depending on the environment. High-resolution single cell measurements show that genetically identical cells can be different from each other even in a homogeneous environment, leading a spectrum of phenotypes with different cellular fitnesses that can reversibly switch between phenotypes. How population-level properties emerge from single cell behavior and how mutants emerge and spread in such populations is unclear. In this thesis, we developed mathematical models to study (i) the effect of time-delay needed for a mutation in a regulator gene influence an effector protein and the long term population fitness, (ii) how optimum population fitness behaves as a function of the growth rates of the various phenotypes for different durations in fluctuating environments. Our work shows a paradoxical outcome of evolution where mutations in a regulator gene interact with gene network dynamics and evolutionary dynamics, giving rise to permanent decrease in population fitness. In the other scenario of fluctuating environments, a previously predicted optimum exists for wider parameter regime if the environmental durations are long and narrow regime for short environmental durations. We also find that a mutant, which randomly evolved to match its phenotypic switching rates with environmental switching rates, can sweep the population if the predicted optimum matches with the assumed one, but not otherwise.