Research Interests

My research focuses on understanding the design and function of molecular networks. With the advent of high throughput genomic and proteomic technologies, biology is experiencing an explosion of new experimental results. A major challenge is to use this data to reveal a coherent systems level view of the cell. My objective is to use computational learning techniques to understand the architecture of molecular networks and elucidate a global view of how the molecular network processes combinations of signals to compute and execute a concerted cellular decision and response. My approach uses Bayesian network inference algorithms to reconstruct models of molecular networks by integrating diverse high throughput data. These models are then used to generate predictions of the systems behavior underd different conditions and illuminates the roles of various components causing these behaviors. Recently the approach was successfully applied to the derivation of causal influences in human signaling networks. The method automatically discovered de novo, most traditionally established influences between the measured signaling components, as well as discovering novel inter-pathway crosstalk, which was experimentally verified.