Our research interests can be broadly defined as building global mechanistic models of cancer progression. We use an integrated systems approach, where we combine advanced computational network methodology with experimental analyses. To this end, a major part of our research focus is dedicated to the development of novel computational methods for automated extraction of testable mechanistic models from large genome-scale data and their implementation in user-friendly software for community use.

One research interest is delineating molecular networks that support tumorigenic platform under specific oncogenic contexts. Our recent work on EGFR family-driven cancers reveals an extensive interplay of the EGFR signaling network with the metabolic and signaling pathways involved in the endoplasmic reticulum adaptive stress response. Current work in the lab is focused on detailed characterization of the dynamic interplay between these processes in driving the tumorigenic phenotype.