We will build patient-specific Logic models of the signaling networks of prostate cancer patients, and use them to develop novel combination therapies
Translation of high-throughput technologies into therapeutic advances in the clinic still poses considerable challenges. These include tumor heterogeneity and the integration of multiple types of molecular data into predictive mathematical models. Prostate cancer, for example, presents an evident clonal diversity, and it is an urgent clinical need to distinguish the many indolent tumors from the minority of lethal ones.
The European H2020 PrECISE (Personalized Engine for Cancer Integrative Study and Evaluation) project aims to improve patient risk-stratification and treatment in prostate cancer by developing new computational approaches that exploit molecular, clinical and prior knowledge biological data.
Building upon our experience on pathway resources and modeling, we will work on the integration of prior biological knowledge with different types of molecular data and its translation to actionable mechanistic models. These models will be used to identify mechanisms of disease and treatment. In particular, we will use our approaches for logic modeling to model, draw inferences and make predictions about cell signaling pathways relevant to the biology of prostate cancer.
Besides our specific contributions to logic modeling, Julio Saez-Rodriguez also serves as scientific coordinator of the consortium:
Project website: http://www.precise-project.eu