Department of Chemistry and the James Franck Institute
My group and I develop theoretical and computational approaches to understand the physical chemical basis of complex behavior in living systems. We are particularly interested in understanding how cells harness energy from their environments to organize their molecular interactions in space and time. To this end, we are working in close collaboration with experimental researchers to design and analyze quantitative measurements of living systems, and, in turn, implement predictive physical models. One feature of biological dynamics that makes this challenging is that they span a hierarchy of length and time scales ranging from Ångstroms and femtoseconds to millimeters and days.
In molecular simulations, bridging these gaps requires increasing the exploration of states that are visited relatively rarely (e.g., transition states) while still enabling recovery of unbiased statistical averages. We developed some of the most general and efficient methods available for accelerating the convergence of properties of microscopically irreversible models (nonequilibrium umbrella sampling and steered transition path sampling); we are now working with applied mathematicians to analyze these methods rigorously to improve and extend them. Additionally, we are exploring machine learning techniques to aid in interpreting simulations and connecting them with experimental observables.