The evolutionary dynamics of drug resistance in host-pathogen interactions

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Infectious diseases remain one of the most significant threats to human health. Many pharmaceutical interventions (e.g., antibiotics and other chemotherapeutic agents) have been developed to slow or stop the spread of disease, but it is now abundantly clear that pathogen evolution often renders these tools ineffective. At the center of this issue is the more fundamental question in evolutionary biology of the conditions that generate the greatest evolutionary response to novel selective agents. Indeed this question also lies at the heart of many other important applied problems, from the optimization of directed evolution approaches for protein and enzyme engineering, to understanding when species will adapt to climate change as opposed to going extinct. The proposed research addresses this important issue in the context of drug resistance. We will develop mathematical theory to predict the evolutionary consequences of different drug interventions, and address three main questions: (1) What factors make it optimal to use high versus low drug concentrations to best control resistance? (2) What role does temporal and spatial variation play in the emergence of resistance? (3) Can we manipulate ecological interactions to make populations evolution-proof? As part of training, my students will also conduct experimental tests of some aspects of the theory, in collaboration with a colleague who works with a mouse model system of malaria. In the longer-term our hope is that this research will lay the fundamental foundations for more clinically oriented studies carried out by health-care scientists.