Develop the theory of microscopic nonequilibrium energy (and information) transduction and its optimization

All living organisms face several fundamental physical challenges in their everyday existence.  Among these are: the maintenance of order despite the propensity of everything to eventually get messy (a.k.a., the Second Law of Thermodynamics); and the performance of mechanically-demanding tasks with the incredibly jiggly materials at hand (a.k.a., proteins).  Our theoretical and computational biophysics research focuses on determining in this context the design principles for effective operation of molecular machines (proteins that convert between different forms of energy, but are 100 million times smaller than the car engines that engineers already know how to design).  More specifically:  what are the physical limits on how well they can operate; what kinds of designs achieve these limits; and (in collaboration with experimentalists) do real-live evolved biomolecular machines actually conform with these theoretical predictions?  

We hypothesize that the selective advantage associated with efficient energy transduction and information processing has provided a strong evolutionary force driving optimization of these properties in biological systems. Thus theoretical analysis of energetic and information-theoretic efficiency in microscopic model systems far from equilibrium should make strong and testable predictions about the design of biomolecular machines. We explore optimal control and response in model systems such as ATP synthase, the pivotal machine for cellular ATP synthesis.