Sarath Chandra Janga
School of Informatics
Indiana University, USA
Sarath Chandra Janga obtained his Bachelors and Masters degrees in Bio-chemical Engineering and Biotechnology from the Indian Institute of Technology, Delhi. Following which he moved to Mexico to work as a research associate at the Center for Genomic Sciences , National Autonomous University of Mexico (UNAM) from 2003 to 2007. Subsequent to which he obtained a PhD in Molecular and Systems Biology from the MRC Laboratory of Molecular Biology & Darwin college, University of Cambridge, UK from 2008-2010. After finishing his doctoral work on employing computational and graph-theoretical approaches to studying problems in molecular and cellular biology, Sarath moved to states as an independent research fellow at the Institute of Genomic Biology, University of Illinois at Urbana-Champaign working on the development of data mining and computational approaches for identifying drug molecules. He is currently an Assistant Professor of Informatics, Molecular and Medical Genetics at the Schools of Informatics and Medicine, Indiana University. He has published ~50 research manuscripts, on various aspects of prokaryotic and eukaryotic biology in the fields of computational molecular and systems biology.
Understanding gene regulatory mechanisms in eukaryotic organisms; Evolution, prediction and organization of operons and regulons in prokaryotes; Understanding regulatory networks and their topological properties in prokaryotes and eukaryotes; Using computational approaches to infer functional relationships between gene products to improve annotation strategies; Integrating diverse kinds of biological interaction datasets like genome-wide associations, protein-protein, regulatory and signal transduction pathways together with text/data-mining techniques to understand dynamic and systems level aspects of regulation and their implications for disease; Mining genomes for novel antibiotic producing pathways and to understand mechanisms of drug resistance; Applying network-based approaches in understanding disease biology, drug discovery and in personalized medicine settings.