Identification and Modeling of Clinical Side Effects of Drugs From Large In Vitro Data Sets Using High-Dimensional Projection Spaces


Most biological characteristics and functions arise from complex interactions and are controlled by various intrinsic and environmental mechanisms. As a consequence, the detailed understanding of disease mechanisms and drug action requires multi-level approaches, which link variables derived from omics data to clinical phenotypes and thereby address cell processes in the context of whole tissues, organs or even patients.

The goal of this project is to develop a model that can connect the dynamics of an in vitro stress response with clinical effects in patients. Can gene expression profiles of cell line experiments be connected to gene expression patterns in tumor biopsies? Can cell line gene expression shifts hint towards patient outcome, such as recovery or the occurrence of toxic side effects? This project aims to establish a quantitative model for the prediction of therapy outcome by investigating these expression shifts and their relation to in vivo patterns.