Predicting Cancer Phenotypes
This project aims to significantly advance our ability to study the complex mechanistic underpinnings of cancer using integrated multi-omic network models. Towards this goal, we are building metabolic networks relevant to cancer biology to serve as a mechanistic basis on which to ground statistical analyses of experimental multi-omic data. This collaborative systems-based approach for integrating and analyzing multi-omic information from regulatory network topology, signaling and metabolic pathways will lead to…
Cancer
Complex diseases like cancers are heterogeneous (unique both within and across individuals) and dynamic (changing over time) diseases that arise from perturbations to multiple biological networks at different levels of organization. One of the ways we profile multiple systems at once is using various -omic technologies to profile an individual in many different ways. The central idea is that we need to personalize our treatments to each individual. Ultimately, we…
