Julia Pelesko

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  A Combined Numerical, Mathematical, and Empirical Model to Predict the Evolution of Drug Resistance
  Julia Pelesko

  Due to the evolution of resistance, drug therapies often fail to treat ailments such as bacterial infections, cancer, and viral diseases, despite initial successes. Improvements in next generation sequencing technologies already allow us to pinpoint resistance conferring mutations. Since evolution is a stochastic process, however, each evolutionary trajectory, or sequence of advantageous mutations, occurs with a certain, currently unknown, probability. By predicting the likelihoods of each trajectory, we can design treatment plans to genetically steer populations away from a resistant phenotype. To this end, we have constructed a morbidostat, an automated continuous culture device, to monitor the evolution of resistance. Using fitness data gathered from the morbidostat, we will parameterize our numerical Agent Based Model (ABM) model and Fokker-Plank mathematic model to predict under what conditions certain trajectories occur. In light of these results, we will update treatment plans for morbidostat experiments and monitor the resulting evolutionary dynamics to verify our predictions.