Modelling microbial evolution

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Bacteria, viruses and other microbes can evolve much more rapidly than humans.  This allows researchers to watch evolution occur in the laboratory, a technique called "experimental evolution".   As these microbes adapt to life in the test tube, researchers watch evolution unfold before their eyes.  These techniques have turned evolution into an experimental science, allowing scientists to address some of our most fundamental questions about evolution in the laboratory.  This field has expanded so quickly, however, that a mathematical framework describing the experiments hasn't yet emerged.  The experiments have, at the moment, out-stripped the theory, and as a result the power that experimentalists have to interpret and test their results has been short-changed.  The first goal of this proposal is to develop a mathematical framework for the analysis of experimental evolution.  This is an almost untouched question in an extremely new and active research area; it is a chance for Canada to step in at the ground level and produce truly innovative and important work. Rapid microbial evolution is also a source of serious public health concern.  In particular, viruses such as HIV rapidly adapt in response to pharmaceuticals until the drug in no longer effective against the virus.  This is especially problematic when drugs are not taken exactly as prescribed, that is, patient "adherence" to drug therapy is low.  Since the best current therapy for HIV may involve up to 17 pills per day, perfect adherence is extremely difficult.  The second goal of this proposal is to use mathematical models to estimate the degree of adherence that is necessary to prevent drug resistance from emerging.  This is a question that is very difficult to answer using clinical studies.  As well as examining the effects of random missed doses, we will also consider intentional "drug holidays".  Some researchers have suggested that such holidays could briefly increase the levels of virus in the patient, thus boosting the immune system in a way similar to a vaccine.  We will use careful mathematical models for specific anti-HIV drugs to predict the best timing for drug holidays.