Antibiotic resistance is a growing public health threat.  The emergence of resistance far outpaces the development of new drugs, underscoring the need for new strategies aimed at slowing the resistance threat.  In this talk, I’ll discuss our group’s ongoing work to understand the evolution of drug resistance in E. faecalis, an opportunistic bacterial pathogen, using quantitative experiments and theoretical tools from statistical physics and dynamical systems. By combining laboratory evolution with simple mathematical models, we show that unconventional strategies–including aperiodic drug dosing, spatially distributed selection pressures, or adaptive containment protocols–can significantly slow resistance under a surprisingly wide range of conditions. These approaches exploit common (but sometimes neglected) features of microbial evolution–ranging from drug-drug correlations in resistance profiles to spatial heterogeneity and resource competition–to steer adaptation through potentially high-dimensional phenotype spaces.  At the same time, the results paint an increasingly nuanced picture of the spatiotemporal dynamics of microbial populations across multiple length and time scales.