Research Summary The rates of population growth for individual bacterial species are partially determined by the permissiveness of external environmental conditions. Even under the most ideal conditions, however, different species are capable of dramatically different rates of growth that range from minutes to days. Which genomic features contribute to this capacity for growth, and how environmental variability constrains and facilitates the evolution of these features remain largely unknown. Currently, most molecular studies focus on how mutations to proteins and regulatory sequences affect cellular growth and fitness measured over hours and days. Evolutionary methods, by contrast, are most powerful when studying long time-scales associated with the fixation of mutations. The adaptation and evolution of bacterial populations over intermediate time-scales on the order of weeks, months, and years is thus difficult to study within existing frameworks, and these time- scales may profoundly influence the evolution of growth rates and associated genomic features. Here, I will investigate how environmental variability can constrain and facilitate the evolution of gene regulatory mechanisms. Mathematical models of cellular growth are capable of making informative predictions about physiological growth under various conditions ranging from nutrient-poor to nutrient-rich environments. I will extend existing models to consider how the translational and nutritional constraints that they identify may ultimately arise from—and partially shape—genome sequence evolution. I will use a combination of mathematical modeling and empirical bioinformatics to investigate how selection for optimal translational control strategies between different classes of genes may arise according to whether genes are dominantly expressed during periods of slow- or rapid-growth. To connect these results to evolutionary differences in growth capabilities between species, I will compile separate databases of maximal growth rates measured in vitro and in situ to determine how the phenotype of growth capacity has evolved across the bacterial phylogenetic tree. Finally, I will apply and develop phylogenetic comparative methods to determine the relevant time-scales for changes in growth strategies between species, and the genomic features that predict (and are predicted by) these changes. This project will form the foundation for the continued development of evolutionary methods that explicitly incorporate cellular growth and growth rate variability across time. Ultimately, understanding the interplay between cellular growth, environmental variability, and translational regulation will lead to improved strategies for administering and cycling therapeutics to fine-tune selective pressures on individual proteins, pathways, and bacterial populations.