Ph.D., Mathematics, University of California-Berkeley
B.A., Mathematics, San Jose State University
Predictive models of molecular interactions and protein design
Molecular interactions provide the essential language of biochemistry. Gene regulation, signaling and other physiological communication processes are controlled by networks of such interactions. It is natural to examine interactions in terms of the individual behaviors, which are largely dictated by structural features and random fluctuation. Because kinetic rates can be altered by environmental conditions such as temperature, and the basis for this is largely structural, the system-wide behavior is intimately tied to structural detail. Our goal is to use predictive models to explore structural characterizations of protein association. The use of computational tools based on physics and experimental data helps us uncover mechanisms of protein association, predict the effects of mutations, and explore protein design.