Study of Microbial Genome Structure and Application to Pathway & Network Inference:

We are interested in understanding both the micro- and macro-structures of microbial genomes through computational studies and experimental validation, and in understanding why microbial genomes are organized the way they are. We are also interested applying the knowledge and information gained through such studies to prediction of pathways and networks in microbes.

Cancer Computational and Systems Biology:

We are interested in developing computational and analysis techniques in support of (a) identification of biomarkers for a number of human cancers, detetable through imaging, analysis of serum/urine samples, (b) understanding the relationships between (computationally identifiable) genomic features and cancer formation and development, and (c) cancer epigenomic studies. Our work involves microarray gene expression data analyses, comparative genome analyses and analyses of other experimental data.

Computational Methods for Protein Structure Prediction and Modeling:

We are interested in developing effective computational methods for protein fold recognition, protein structure prediction and modeling, and protein complex prediction; and applying these tools to solve real structural biology problems. We are also interested in developing hybrid methods for protein structure solution using information from derived from computational tools and partial experimental data, including NMR and X-ray crystallograpohic data.

Our research work is currently sponsored by NSF, DOE, NIH, Georgia Research Alliance, Georgia Cancer Coalition and the University of Georgia. In addition, our work had been generously supported by Oak Ridge National Lab and Pacific Northwest National Lab.