Proteomics, Systems Biology, Stress physiology and cellular responses
The first genome sequence for a free-living organism was completed in 1995, and subsequently many large-scale genome sequencing projects for a number of organisms including human have been completed. Although the completion of human genome sequencing project was remarkable, it was insufficient to unravel the mysteries of essential biological processes. A more meaningful understanding of gene expression can be achieved through characterization of the products of expression, the proteins, which are essential determinants of biological function. Gene expression is regulated by post-transcriptional and post-translational modifications due to which the number of proteins expressed in a cell are many times larger than their genomic counterpart. The term proteome describes the protein complement expressed by a genome of a given cell at a given time including the set of all protein isoforms and modifications. Analogous to genomics, proteomics describes the study and characterization of complete set of proteins present in a cell, organ or organism at a given time. Several techniques used in proteomics typically aim to elucidate the expression, localization, interaction, structure, biochemical activity, and cellular roles of proteins. Systems biology and proteomics strive to create detailed predictive models for molecular pathways based upon the quantitative behaviour of proteins. While understanding these dynamic networks provides clues into the consequence of aberrant interactions and why they lead to diseases like cancer, collecting biochemical data about protein behaviour at scale has been daunting. Our laboratory aims to apply high throughput proteomic techniques such as protein microarrays, two-dimensional gel electrophoresis, mass spectrometry etc. for biomarker discovery, enzyme substrate identification, protein-protein interactions and drug target discovery. Information obtained from research program is also used for in silico studies and computing models to enhance our understanding in systems approach.