This research focuses on understanding how bacteria respond to antibiotics and investigates non-genetic differences—such as variations in individual cells’ growth rates—that emerge under these conditions. Over the last century, it has become widely recognized that non-genetic variation is critical to understand, as it can influence the outcome of antimicrobial therapies and the evolution of antibiotic resistance. Moreover, it is deeply connected to fundamental questions about how cells grow and divide. The project combines cutting-edge experiments, where single-cells are imaged under varying conditions, with mathematical modeling to predict non-genetic differences in growth and biochemical composition of E. coli. These predictions will deepen our understanding of antibiotic resistance and microbial physiology while laying the foundation for broader efforts to combat drug resistance and improve treatment outcomes. The project is a collaboration between a mathematician and a microbiologist and will provide rich opportunities for undergraduate and graduate student training in quantitative biology. This project develops predictive models that link single-cell gene expression and growth dynamics to population-level behavior in bacterial systems under antibiotic stress. Focusing on the tetracycline resistance operon in E. coli, the research integrates stochastic modeling of gene expression, growth, and size regulation with single-cell data from microfluidic experiments. Aim 1 mo