Theoretical Chemistry Seminar: Dr. Grant Rotskoff, New York University (Host: Tom Markland)
About the Seminar
"Robust and Controllable Nonequilibrium Self-Assembly"
Self-assembly is typically described as an equilibrium process in which molecular interactions dictate the final structure of an ensemble of components. In some cases, this paradigm suffices, but we rarely have the required precision to design interactions specific enough to reliably produce complex structures. Alternatively, if we control a physical system with an external nonequilibrium protocol (e.g., applying an electric field, maintaining concentration gradients) we can robustly assemble structures that would not be kinetically accessible to an equilibrium system. I will illustrate this alternative path to self-assembly dynamics with two examples: First, bacterial microcompartments, organelle-like, proteinaceous structures found in photosynthetic bacteria, assemble to encapsulate enzymes crucial for carbon fixation. Geometric similarities between microcompartment shells and viral capsids have inspired models of the assembly process that posit that the microcompartment represents a stable, equilibrium arrangement of its constituent proteins. This assumption does not hold, however, for shells that lack intrinsic curvature, which experiments suggest is the case for microcompartments. Second, I will describe a model of colloidal self-assembly that highlights fundamental relationships among kinetic accessibility, the energetic costs of driving a system, and the yield of a desired structure.
About the Speaker
Grant Rotskoff is a James S. McDonnell Fellow working at the Courant Institute of Mathematical Sciences at New York University. As a theoretical biophysicist, he studies the nonequilibrium dynamics of living matter with a particular focus on self-organization. His work often involves developing theoretical and computational tools that can probe and predict the properties of physical systems that are driven away from equilibrium. Recently, he has focused on characterizing and designing accurate machine learning techniques for biophysical modeling. Prior to his current position, he completed his Ph.D. at the University of California, Berkeley in the Biophysics graduate group as an NSF Fellow. His thesis, which was advised by Phillip Geissler and Gavin Crooks, introduced new theoretical tools for understanding nonequilibrium control of the small, fluctuating systems encountered in molecular biophysics. He also worked on coarse-grained models of the hydrophobic effect and self-assembly. Grant became interested in biophysics as an undergraduate at the University of Chicago, where he received an S.B. in Mathematics, while working on free energy methods for large scale molecular dynamics simulations with Gregory Voth and Benoit Roux.