About Us

Welcome to the laboratory of Saurabh Sinha at the University of Illinois, Urbana-Champaign.

Our research belongs to the inter-disciplinary field of Bioinformatics, which provides the computational infrastructure for modern molecular biology as it rapidly transforms into a quantitative science. The problems that we focus on pertain to the phenomena of gene regulation and its evolution. Gene regulation refers to how genes in a cell are switched on or off to determine the cell’s functions. It is central to an extraordinary range of biological phenomena from development to disease, as well as the evolution of diverse life forms. We develop innovative computational methods, based on probabilistic inference, machine learning, and biophysics-inspired models, to answer unsolved and topical questions related to gene regulation in diverse biological processes. We collaborate extensively with experimental biologists for confirmation of predictions made by our models.

Principal Investigator: Saurabh Sinha.


Multi-omics integration identifies regulators of colorectal cancer invasiveness

S. Ghaffari, C. Hanson, R.E. Schmidt, K.J. Bouchonville, S.M. Offer, S. Sinha (2021). An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes. Genome Biology 22(19). [Free full text]

Perspective on Gene Regulatory Networks in Behavior

S. Sinha, B.M. Jones, I.M. Traniello, ... G.E. Robinson (2020). Behavior-related gene regulatory networks: A new level of organization in the brain. PNAS , 201921625. [Free full text]

A Cloud-based knowledge engine for genomics

C. Blatti, A. Emad, M.J. Berry, ... C.B. Bushell, S. Sinha (2020). Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform. PLoS Biology 18(1): e3000583. [Free full text]

Robot uses Bayesian optimization to plan experiments without human intervention

M HamediRad, R Chao, S Weisberg, J Lian, S Sinha, H Zhao (2019). Towards a fully automated algorithm driven platform for biosystems design. Nature communications, 10 (1), 1-10. [Free full text]

Graphical models help identify major transcriptional regulators of drug response variation

Hanson, C., Cairns, J., Wang, L., & Sinha, S. (2018). Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res, 28(8), 1207-1216. [Free full text]