Model Identification for Tumor Drug Response Mechanisms Using Heterogeneous High-Dimensional Multi-Omics Assays
The proposed research project is intended to establish a combinatorial network re-engineering approach that combines the benefits of two complimentary modeling methods, thus closing the modeling gap present in prediction of both efficacy and precise modes of action of drugs.
Although the detailed characterization of modes of action and off-target effects of molecularly targeted drug compounds is pivotal to drug development, there is a profound lack of efficient methods using genome wide data from stationary, high-troughput data sets from heterogeneous cellular assays. Using the model structures available today, drug effects mediated by a-priori unknown cellular mechanisms may not be sufficiently assessable, and novel unsupervised network reconstruction algorithms working with data obtained from broad-scale transcriptome or proteome profiling are needed as complementary methods. Therefore, this project is focused on developing a combinatorial network reengineering approach, merging the experience in reengineering and modeling of drug effects on signaling networks of the Mitsos Lab with the mathematical tools for genome-wide modeling of cell status and network topology reengineering established at AICES.