In PN2-1, 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. 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. PN2-1B 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 PN2-1A to ultimately test if clinical patient outcome can be predicted.
|Project Number||PN 2-1B|
|Project Name||Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success|
|Project Duration||December 2019 – May 2023|
|Project Leader||Nicole Radde|
|Project Members||Vincent Wagner, PhD Researcher|
|Project Partners||Cooperation is planned with PN2-2 by exchanging methodology for model calibration, UQ or sensitivity analysis and by defining interfaces between our models and the multi-scale liver tissue tumor models developed in PN2-2. The work carried out in PN2-1A/B can serve as a use case for these endeavors. We also actively collaborate with PN2-5 on workflows for the calibration of enzyme kinetic models and epigenetic methylation processes. Further scope for interactions and intellectual exchange is foreseen or already explicitly planned at the technological level with PN1 (fusing models), PN3 (multi-scale integration), and PN4 (joint data-driven modeling).|