Research Overview

Our research is focused 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 work at the systems level 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.

Molecular Design of Drug Resistance Resistant Inhibitors
	Understanding Oscillatory Biological Systems using Dynamic Optimization Techniques
Dynamic Optimization as a Method to Probe Biological Networks and Their Behavior
	Antibody Affinity Maturation Using Computational Protein Design
Parameterizing Biological Networks
	Creation of a Biological Calcium Sensor by the Rational Redesign of Calmodulin
Biomolecular Transduction of Mechanical Signals: Simulation Study of Force-modulated Protein Activit
	Using Optimal Time Varying Inputs to Probe Biochemical Signaling Networks
Information Theoretic Approaches to Identifying Informative Subsets of Biological Data
	Decoding the Mechanism of Oscillations in Feedback-Connected Biological Networks
Computational Analysis and Prediction of Phosphopeptide Binding Sites