Jason Papin
About
Jason Papin develops computational models of cellular networks and performs high-throughput experiments to characterize biological systems relevant to human disease. After his training in Bioengineering at the University of California, San Diego, Jason Papin joined the faculty at the University of Virginia in 2005. He is now a Professor in the Department of Biomedical Engineering.
His lab works on problems in systems biology, metabolic network analysis, infectious disease, toxicology, heart disease, and cancer, developing computational approaches for integrating high-throughput data into predictive computational models. He manages a lab with both experimental and computational activities and his research group has had continuous support with funding from the National Institutes of Health, National Science Foundation (including as a CAREER award recipient), Department of Defense, Department of Energy, and several private foundations and companies. Jason is an elected fellow of the Biomedical Engineering Society and the American Institute of Medical and Biological Engineering.
Jason serves as the Co-Editor-in-Chief of PLOS Computational Biology as well as on the editorial board of other journals. His service to the scientific community also includes effort as a past elected member of the Board of Directors of the Biomedical Engineering Society, previously as a standing member of the Biodata Management and Analysis (BDMA) NIH study section, and numerous other review panels of federal funding agencies.
His teaching and mentoring have been recognized with receipt of awards for excellence in undergraduate, graduate, and postdoctoral training. He is the past director of the Biomedical Engineering graduate program at the University of Virginia.
Jason’s work also involves many translational activities with recognition as an inventor on several issued patents and consulting with biotechology companies.
Education
Ph.D. University of California, San Diego 2005
M.S. University of California, San Diego 2002
B.S. University of California, San Diego 2000
Network modeling of biological systems to predict drug targets and understand mechanisms of disease.