Molecular biophysics theory and methods; coarse-grained modeling, simulations and analyses of the structure & dynamics of biomolecular complexes and assemblies; elastic network models and normal mode analysis; statistical mechanics of macromolecules; sequence-structure-function relations in biomolecular systems; enzyme-substrate, protein-DNA and protein-drug binding and interactions; mathematical modeling of cell cycle signaling and regulation, and protein-protein interaction networks

I am primarily interested in understanding the mechanisms that control gene transcription regulation. My group develops statistical methods and computational models for the study of the evolution of gene transcription circuits as well as the molecular basis of protein-DNA interactions. In vivo and in vitro experimentation is used both for data generation and method validation.

A striking set of specific and non-specific interactions encoded in the protein structure tolerates binding only to a unique substrate. My main research interests focus on modeling the physical interactions responsible for molecular recognition, and in the development of new technologies for structural prediction, their substrates and supramolecular assemblies. Any progress in these fundamental problems is bound to bring about a better understanding of how proteins work cooperatively in a cell, promoting breakthroughs in every aspect of the biological sciences.

Developing computational models and methods to improve the understanding of major interactions and allosteric mechanisms that underlie the proper functioning of biomolecular systems. In particular (i) developing information-theoretic concepts for determining the probabilistic rates and pathways of information flow in biomolecular systems both within multicomponent structures and at the cellular level (protein-protein interaction networks); and studying the sequence of events possibly induced by information flow; (ii) designing and interpreting FRET based experiments to explore molecular interactions and correlations and assessing their functional implications; and finally (iii) developing novel methods originating from computer vision for analyzing, refining and interpreting biomolecular images in terms of the structure, dynamics and function of the observed systems, focusing on cryo-electron microscopic measurements.

I am interested in developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. Together with collaborators as Los Alamos National Laboratory I have developed a simulation framework called BioNetGen that allows rule-based specification of biochemical reaction networks and provides both deterministic and stochastic modeling capabilities. My current research includes the development of specific models of signal transduction and the development of new stochastic simulation algorithms that will greatly broaden the scope of models that can be developed. Other research areas include model reduction, parameter estimation and uncertainty analysis, and automated model construction from databases of protein interactions.

The broad objective of our research is to help cure human diseases by developing bioinformatics/computational and experimental methods to study gene functions. Recent studies link non-protein-coding RNAs to cancer and other diseases. Noncoding RNAs such as microRNAs are thought to post-transcriptionally regulate a large number of human genes. The research on non-coding RNAs is likely to provide new diagnostic and prognostic markers and eventually therapeutic targets for the treatment of human diseases. We also aim to develop methods to aid computer aided drug design efforts. Our research strategy is to apply bioinformatics/computational methods to formulate reasonable hypotheses about interesting biological problems and subsequently conduct experiments to test them.

The general direction of my research is theoretical development, computational analysis, and experimental validation of quantitative models that explain cellular morphogenesis from the systems standpoint, integrating molecular motor-driven transport, cytoskeleton dynamics, and cell signaling.

Structure and function of proteins by the energetic and statistical approaches. Development of modeling of solvation, methods for calculating the entropy and the free energy of macromolecules and fluids (water), and simulation and conformational search techniques for protein systems. These methods are components of a new statistical mechanics methodology for treating flexibility applied to loops, peptides, and active sites to understand protein-protein and protein-ligand recognition processes (e.g., antibody-antigen interactions) and to analyze NMR and x-ray data of flexible molecules.

Asymmetry in the distribution of attributes along biological sequences generates signals with characteristic frequency and phase spectra. Asymmetry in the distribution of contacts in 3-dimensional models also generates signals with characteristic spectra. In some cases, these spectra are correlated. My research attempts to predict tertiary structure from these correlations. The long term goal is go develop an alignment-independent method for protein classification. The methodologies employed include n-gram analysis, Fourier analysis, eigenfunction decomposition and all poles spectral density estimation. In related research, correlations between the periodicity of pairwise relationships in molecular dynamics simulations and the results of Gaussian network analysis are compared.