We focus on the analysis of complex biological systems at both the molecular level and the systems level.
Our molecular work concentrates on the structure and properties of proteins, nucleic acids, and their complexes. Investigations probe the sources of stability and specificity that drive folding and binding events of macromolecules. Studies are aimed at dissecting the interactions responsible for the specific structure of folded proteins and the binding geometry of molecular complexes. The roles played by salt bridges, hydrogen bonds, side-chain packing, rotameric states, solvation, and the hydrophobic effect in native biomolecules are being explored, and strategies for re-casting these roles through structure-based molecular design are being developed. Our systems work involves the construction and analysis of correlated patterns of gene expression and their relation to biochemical regulatory networks and signal transduction pathways in cells. Much of this work is motivated by the enormous advances in genome science and in the availability of parallel arrays of gene expression data. The methods of theoretical and computational biophysics and approaches from artificial intelligence, applied mathematics, and chemical engineering play key roles in our work.
Our goal is to develop a model for the binding, internalization, and tumor-killing dynamics of liposome-enclosed doxorubicin targeted to cancer cells and develop design principles for creating more effective therapeutics.
Deep neural networks are applied to predicting protein-ligand binding affinity by learning representations of atoms, atomic interactions, and inter-molecular interfaces.
We develop, parameterize, and validate a model for tumor growth inhibition using in vivo mouse data and study the effects of modeling uncertainty and inter-individual variability on drug candidate efficacy predictions.
We use computational models of biological pathways in disease to estimate the range of possible responses to various hypothetical drugs, given patient variability and limited pathway-relevant data.
