RAS Pathway in Cancer and Disease

The RAS pathway is particularly well-suited to serve as our 'model system' for the systems biology of human disease. With an important role in cancer and more than three decades of intense study, the RAS network is exceptionally well-characterized biochemically. This provides important information for building our mathematical and computational models. Additionally, well-validated, readily available reagents for the RAS pathway facilitate experimental testing of our computational predictions.

Our previous work on the RAS pathway includes the use of our models to formulate hypotheses about RAS in cancer that we tested and confirmed experimentally (Stites EC et al, Science, 2007; Stites EC et al, Cell Reports, 2015). We will be investigating several additional hypotheses about the RAS pathway in cancer, in genetic diseases, and in autism.

RAS Model

Oncogenic Kinase Signaling

We are developing new approaches to model oncogenic kinases, with a focus on kinases in the RAS signaling network, like RAF (Hu J, et al, Cell, 2013) and EGFR (Stites EC et al, Biophysical Journal, 2015). We will be testing several new hypotheses about kinase signaling in disease (Stites EC, Science Signaling 2012), including ideas for how to better target these kinases therapeutically.

RAS Model

Systems Biology of Disease

Exciting potential applications for computational models of disease-associated networks include roles in drug development (systems pharmacology), in matching patients with the right drugs for their genetic background (personalized medicine), and for interpreting patient-specific genomic and molecular characterization (computational pathology).

The RAS model has been successfully applied to problems in each of these areas, but we want to do this for more proteins, more networks, and more diseases. The computational methods will necessarily vary for different diseases for reasons ranging from what information is available to what specific questions will be asked of the model. The development and interpretation of models requires a thorough understanding of both the biology and the mathematics. The lab has multiple opportunities for individuals eager to tackle new systems.

We anticipate that analyses of such models will provide deeper insights into mechanisms of disease, as well as help identify new strategies for preventing and treating disease.