UC Berkeley

Living systems, even at the scale of single molecules, are constantly adapting to changing environmental conditions. The physical response of a nanoscale system to external gradients or changing thermodynamic conditions can be chaotic, nonlinear, and hence difficult to control or predict. Nevertheless, biology has evolved systems that reliably carry out the cell’s vital functions efficiently enough to ensure survival. Moreover, the development of new experimental techniques to monitor and manipulate single biological molecules has provided a natural testbed for theoretical investigations of nonequilibrium dynamics. This work focuses on developing paradigms for both understanding the principles of nonequilibrium dynamics and also for controlling such systems in the presence of thermal fluctuations.

Throughout this work, I rely on a perspective based on two central ideas in nonequilibrium statistical mechanics: large deviation theory, which provides a formalism akin to thermodynamics for nonequilibrium systems, and the fluctuation theorems which identify time symmetry breaking with entropy production. I use the tools of large deviation theory to explore concepts like efficiency and optimal coarse-graining in microscopic dynamical systems. The results point to the extreme importance of rare events in nonequilibrium dynamics. In the context of rare dynamical events, I outline a formal approach to predict efficient control protocols for nonequilibrium systems and develop computational tools to solve the resulting high dimensional optimization problems. The final chapters of this work focus on applications to self-assembly dynamics. I show that the yield of desired structures can be enhanced by driving a system away from equilibrium, using analysis inspired by the theory of the hydrophobic effect. Finally, I demonstrate that nanoscale, protein shells can be modeled and controlled to robustly produce monodisperse, nonequilibrium structures strikingly similar to the microcompartments observed in a variety of bacteria.