Sudin Bhattacharya
Contact
sbhattac@msu.edu
517-884-6952
View publications on MSU Scholars
Systems Biology Division
About
Sudin Bhattacharya leads a lab that conducts research at the interface of computation and biology, using quantitative tools to study the signaling and transcriptional networks that regulate cell fate and the perturbation of these networks by environmental pollutants.
Dr. Bhattacharya joined MSU as an assistant professor in November 2015. A native of Kolkata, India, Dr. Bhattacharya completed his undergraduate degree at Jadavpur University, his master’s degree at the University of Kentucky, and his Ph.D. at the University of Michigan, all in mechanical engineering. He completed his postdoc in computational biology at The Hamner Institutes in Research Triangle Park, North Carolina.
The Bhattacharya Lab
The Bhattacharya Lab uses a variety of computational tools to address fundamental problems across multiple scales in biology and toxicology.
Like many other fields in academia and industry, toxicology is being revolutionized by large data sets and computational modeling. We are applying a variety of predictive modeling tools to understand how tissues, cells and intracellular gene regulatory networks are perturbed under exposure to potentially toxic chemicals.
Major areas of research
Mapping and Modeling Gene Regulatory Networks
We are using large genomic and epigenomic data sets, statistical modeling, and data science tools to map and model nuclear receptor-mediated gene regulatory networks in the liver and immune cells; and trying to understand how toxic exposure alters the structure and function of these networks.
Multi-scale modeling of “virtual tissues”
We are also developing multi-scale, multi-cellular “virtual tissue” models to understand heterogeneous gene expression and progression to toxicant-induced injury in the liver lobule.
Analysis of single-cell gene expression and cell fate choice
We are very interested in the “forces” underlying cell fate choice and dysregulation; and are using single-cell genomics data to derive marker genes predictive of transition to adverse health outcomes.
