(412) 648-3333

Modeling and simulations of the structure & dynamics of biomolecular complexes and assemblies; statistical mechanics of macromolecules; molecular aspects of biochemical and biophysical networks; computer modeling and engineering of enzyme-substrate, protein-DNA and protein-drug binding and interactions; mathematical modeling of cell cycle signaling and regulation, statistical analysis of gene expression arrays.

I am interested in understanding the mechanisms that determine the DNA-binding specificity of the transcription factors. My group develops  probabilistic and computational models of the recognition of DNA by proteins.  Among other applications, these methods are used for the identification of  DNA-regulatory regions in a genomic scale.

 (412) 648-7776

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.

 (412) 648-8607

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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.

 (412) 648-7771

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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.

 (412) 648-3338

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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.

 412-383-9146

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 Associate Professor, Department of Computational Biology, School of Medicine

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.

 (412) 648-3335

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I am interested in a variety of computational methods in bio-molecular-physics and physical chemistry: free energy calculations (e.g., of binding affinities), determination of reaction path(s) & rates, and the utility of simple models of biomacromolecular dynamics. Because current computers -- and those of the foreseeable future -- are quite slow with respect to many biologically important calculations, the basic goal is to generate physical/chemical/biological data more rapidly via new algorithms or reduced models.