Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success

We will develop novel data-integrated solutions to address the gap between the molecular understanding of network-coded death decisions in cancer cells and their higher scale consequences on patient outcome. For this, we will build on preparatory work in which we demonstrated that prototype mathematical models of cell death commitment at the level of the Bcl-2 protein family and at the level of multi-protein apoptosome timers hold potential to predict the responses of cancer cells to established and novel therapeutics, and possibly to predict clinical patient outcome. Linked with the systems-theoretical and technological challenges addressed in PN 2-1B, we will thereby obtain novel, translationally relevant systems medicine tools for the prediction of treatment success in heterogeneous cancers.