Antibiotic resistance is among the most urgent threats to public health. In this talk, I will discuss our group’s ongoing efforts to understand how E. faecalis, an opportunistic bacterial pathogen, responds to antibiotics across multiple length and time scales. First, I’ll describe recent experiments using customized, computer controlled bioreactors to demonstrate that growth inhibition depends strongly on population density for many commonly used antibiotics, potentially leading to bistable treatment outcomes in a pharmacological model of antibiotic treatment.

In the limit of high population densities, subinhibitory antibiotic concentrations can promote formation of biofilms--an effect that reflects a trade-off between antibiotic efficacy and the beneficial effects of cell lysis--while higher doses can shape the single-cell architecture of drug resistant communities. Finally, I’ll overview ongoing work combining laboratory evolution with mathematical modeling aimed at slowing resistance evolution using temporal drug sequences designed to maximize collateral drug sensitivity over different time scales.