Johannes Reiter

Our research in the Translational Cancer Evolution Laboratory focuses on the stochastic biological processes underlying cancer evolution with the goal to improve the prognosis and treatment of tumors. We develop computational methods to learn from large-scale biological data sets and design mathematical models to predict patient outcomes, generate novel hypotheses, and explain observations on a mechanistic level. We apply these methods to genomic data from clinically-annotated patient cohorts to advance precision medicine. For example, we have designed methods to optimize cancer early detection strategies and to find optimal combination therapies to minimize the risk of cancer relapse.