Antibiotics have been major contributors to global health in the past century. However, current challenges facing their use are the rise of antibiotic resistance and decline in new drug discoveries. In order to combat this resistance it is important to have a basic understanding of how antibiotics work. In this talk I will describe how coarse-grained statistical physics style models can be used to inform drug design and treatment strategies. At present a rough, phenomenological classification is into bactericidal or bacteriostatic antibiotics which respectively kill microbes or inhibit their grow. I will discuss how for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. The model reveals that growth-dependent susceptibility of bacteria is controlled by a single parameter characterizing the reversibility of antibiotic transport and binding. This `reversibility parameter' yields a universal growth-dependent antibiotic susceptibility curve which provides a robust classification of ribosome-targeting antibiotics Reference: Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics Philip Greulich, Matthew Scott, Martin R. Evans, Rosalind J. Allen Molecular Systems Biology 11:796 (2015)