ColoNET: A Systems Biology Approach for Integrating Molecular Diagnostics and Targeted Therapy in Colorectal Cancer

The major objective of the project is to accomplish an in silico network of genetic, epigenetic and signaling processes to tailor diagnostics with predictive information for therapies targeting receptor tyrosine kinases (e.g. EGFR) in colorectal cancer. The network model is expected to provide diagnostic guidance for the improvement of therapy response prediction by achieving an in-depth functional understanding of receptor-mediated signaling and downstream processes. The core model will consist of a static colon-specific signaling network model that serves as a framework for integrating detailed kinetic models of MAPK and Wnt pathways, existing genetic/epigenetic information, high-throughput data, knowledge on predictive markers obtained by literature mining, and physiologically relevant data. The dynamics of MAPK and Wnt pathways, which are crucial for targeted therapies1,2, will be subjected to detailed mathematical modeling based on results from focussed experimental measurements. Once detailed models of pathways, key signaling nodes, feed-back loops and marker combinations are established, we will turn the static network into a dynamic model in an iterative process. Finally, this approach should generate a tool for the in silico evaluation of novel markers or combinations of them for an improved therapy prediction. We expect that our model will also be useful for other types of cancer and related predictive diagnostics.