Our research interests lie in the area of bioinformatics. We employ computational methods to elucidate intertwined relationships between metabolite/protein/gene/ESTs sequences, structure, function, interactions, genome, transcriptome and pathways using mathematical modeling approaches i.e., deterministic and probabilistic modeling. The ultimate goal of our research is to obtain new, comprehensive understanding of how structures and functions are coded in molecular sequences & chemical structure and how functions of molecules are orchestrated in a cell. Specifically, we develop and apply novel computational methods for predicting protein structure from sequence, predicting protein function from sequence and structure, predicting protein-protein, protein-DNA, and protein-small molecules interactions by using probabilistic modeling (simulation approach), predicting functional sites in sequences, genome-scale function and structure annotation, transcriptome analysis, analyzing functional units in networks, structure activity relationship, structure toxicity relationship, predictive pharmacokinetics (ADME) and predictive toxicology studies. Data mining, machine learning, visualization, data and knowledge fusion, graphical interfaces etc. are all research areas for which we are developing our methods, databases and tools related to medicinal and aromatic plants.