Our lab focuses on developing algorithms to bridge between computer science and the life sciences. Our work falls in newly-coined areas such as computational structural biology and bioinformatics and combines artificial intelligence, robotics, computational geometry, statistical mechanics, and distributed computing. We investigate from a computational perspective problems concerning sequence, structure, dynamics, function, and interactions of biological molecules.

We develop search algorithms that combine motion planning and kinematics in robotics with statistical mechanics to compute feasible tertiary structures populated by protein and RNA chains. We investigate the relationship between sequence and structure in protein monomers and complexes. Problems we are working on include how protein chains and complexes flex their structures upon association.

One of our research goals is to understand what constitutes function in biological molecules. In particular, what do transitions of structures and dynamics in molecules say about function? We are currently aiming to address this question in the context of some interesting yet diverse peptides with antimicrobial activity. Our approach combines evolutionary computing with molecular search.

Tandem efforts by our lab aim to understand what constitutes function not in isolation but by modeling and analyzing experimentally-determined molecular interactions in a cell. Interesting concepts from graph theory, spatial analysis, statistics, and statistical physics are currently being pursued.

A unified framework that can model the relationship between sequence, structure, dynamics, function, and interactions will allow understanding the behavior of cellular or engineered molecular machines. This is an important step towards using computation to assist in molecular design, whether our goal is to design therapeutics and drug-delivery templates or to construct novel functional materials.