Abstract

The emergence and spread of antibiotic-resistant microorganisms is an increasingly serious global problem. Despite significant advances in our understanding of genetic alterations and molecular mechanisms behind resistance, quantitative models that could accurately predict the response of bacterial populations to antibiotic treatment are rare. In this talk I will discuss our attempts at constructing such predictive, experiment-driven models.

I will first discuss experimental approaches (single-cell imaging, continuous culture systems, automated liquid handling platforms, whole-genome sequencing) that we use to understand short- and long-term bacterial response to a sudden exposure to an antibiotic. I will then show how the experimental results can be understood using physics-inspired models of increasing complexity: from a simple stochastic birth-death model of sensitive and resistant cells to individual-based simulations with explicit modelling of molecular mechanisms of antibiotic’s mode of action. Finally, I will discuss how these models can be used to make testable predictions to falsify different biological hypotheses about the emergence of antibiotic resistance.