Dinesh Barupal

Dinesh Barupal, PhD

About Me

Dinesh Barupal, PhD is an Associate Professor in the Department of Environmental Medicine and Climate Science at the Icahn School of Medicine at Mount Sinai. Since 2020, He is leading the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.ME). His research focuses on understanding exposome-induced metabolic dys-regulations and their relationships with chronic diseases. His group develops and implements novel computational methods for large-scale metabolomics and exposomics studies with a specialization in metabolic epidemiology, computational metabolomics, metabolic bioinformatics, biomedical text mining and the blood exposome. Dr Barupal is a leading computational biologist and exposure data scientist and over the past decade has developed several bioinformatics methods, including ChemRICH, MetaMapp , IDSL.GOAthe Blood Exposome Database  and the Exposome Correlation and Interpretation Database (ECID) to process and interpret metabolomics and exposomics datasets in the context of human metabolic biochemistry and exposure biology. His laboratory has also published several R packages (IDSL.IPA, IDSL.UFA, IDSL.CSA) and Python workflow (IDSL_MINT) to process and annotate high-resolution mass mass spectrometry untargeted metabolomics and exposomics datasets. These resources are available at the lab's Github page. Dr Barupal received his Doctoral and Post-Doctoral training at the University of California, Davis with Prof. Oliver Fiehn. He was a senior scientist in metabolomics and bioinformatics at the International Agency for Research on Cancer (IARC), World Health Organization in Lyon France (2012-2015) and the West Coast Metabolomics Center, UC Davis (2016-2020).  

ASSOCIATE PROFESSOR | Environmental Medicine and Climate Science
Research Topics

Alzheimer's Disease, Biochemistry, Bioinformatics, Cancer, Computational Biology, Environmental Health, Epidemiology, Mass Spectrometry, Metabolism, Metabolomics, Public Health, Systems Biology

Multi-Disciplinary Training Areas

Artificial Intelligence and Emerging Technologies in Medicine [AIET], Cancer Biology [CAB], Disease Mechanisms and Therapeutics (DMT), Genetics and Genomic Sciences [GGS]