Speaker
Description
In initially drug-sensitive populations of pathogens or cancerous cells, resistance emerges during drug treatment in some, but not all, populations. This observation of variable outcomes motivates the use of stochastic mathematical models to describe and predict de novo evolution of resistance. Both rate of appearance and fate of resistant mutants depend on the environment, which varies over the course of drug treatment: firstly due to drug dosing and pharmacokinetics, and secondly because the focal population feeds back on its own environment (by consuming and producing substrates). In this talk, I will first outline basic mathematical equations for the probability of emergence of resistance, and how these equations could incorporate environmental variation over time. I will then describe a theoretical case study modelling the emergence of drug resistance in chronic viral infections. This case study illustrates both a general analytical approach, and an example of how environmental feedbacks influence stochastic emergence of resistance. Next, I will present experimental results with Pseudomonas aeruginosa (an opportunistic bacterial pathogen) that give insights into how the environment shapes emergence of resistance. In particular, initial density of the sensitive population plays a surprisingly complex role. This empirical evidence motivates ongoing extensions to our mathematical models.
