We study bacterial microcompartments to inspire design principles for programmable catalytic bionanoreactors

We study the relationship between environmental conditions and morphologies of biopolymers (e.g., alginate, cellulose, etc.) to tune mechanical, electrical, and thermal properties of biopolymeric materials

We develop new theories and methodologies to analyze out-of-equilibrium (or path-dependent) pathways through the framework of entropy generation

We investigate the formation mechanisms of porous crystals (e.g., metal-organic frameworks) to identify synthesis conditions that selectively control polymorphism

We study the hierarchical assembly and degradation of stimuli-responsive protein-based hydrogels to enable their use for targeted drug/biologics delivery

We study surface-layer (S-layer) proteins, which assemble into nanoporous and paracrystalline lattices on bacterial surfaces. Of particular interest are S-layers that act as virulence factors in pathogenic bacteria

We deploy enhanced sampling methods combined with collective variable discovery to investigate low-dimensional free energy surfaces

We leverage machine learning strategies to facilitate data-driven modeling and simulation

We design computationally-driven protocols for structure prediction of macromolecular complexes

We leverage our expertise in molecular simulations across quantum mechanical to coarse-grained resolutions

Using a combination of molecular simulations and statistical inference techniques, we design computer algorithms to aid protein engineering efforts

We investigate the transient events leading to hierarchical assembly, disassembly, and reorganization

We develop methodologies to derive systematic (or bottom-up) coarse-grained models from atomistic (or other high-fidelity) statistics