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

In PN 2-1, we 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. The overall goal is the development of novel systems medicine tools that can be used to predict treatment success and to optimize treatment strategies for heterogeneous cancer types. PN 2-1B will focus on systems-theoretical methods in order to facilitate the modeling process regarding workflows for model calibration to diverse experimental data on different scales, model evaluation and experiment design. Therefore, we will build on our expertise in statistical learning approaches for parameter estimation, allowing for a consistent uncertainty quantification. Various data on different scales ranging from single cell molecular data up to the tissue level and patient specific information will be used jointly with PN 2-1A to ultimately test if clinical patient outcome can be predicted.