Data-driven approaches for uncovering functional variation in protein interactions

Abstract

Proteins–the "workhorse" molecules encoded by our genes–carry out a dazzling multitude of functions by interacting with DNA, other proteins and various other molecules within our cells. Together these interactions comprise complex networks that differ naturally across cells within an organism, across individuals in a population, and across species. Although such variation is critical for normal organismal functioning, mutations affecting protein interactions are also known to underlie a wide range of human diseases. In my talk, I will present novel computational approaches that explore the extent to which specific protein interactions vary across species, across healthy individuals, and across individuals with disease. Finally, I will show how one of the approaches I have developed can be applied to discover perturbed molecular mechanisms across human tumors, thereby enabling valuable insights that may help guide personalized cancer treatments.

Bio

Shilpa Nadimpalli Kobren is a Bioinformatics Research Fellow in Dr. Isaac Kohane’s lab at Harvard University, where she develops methods that integrate genome-scale sequencing data with clinical health record data to derive insights on the molecular underpinnings of rare human diseases. Her primary research interests sit broadly at the intersection of computer science and molecular biology and are directed at developing interpretable computational methods that further our understanding of molecular functionality within living organisms. Dr. Kobren earned her B.Sc. in Computer Science and Biology at Tufts University as a Neubauer scholar, and her M.Sc. and Ph.D. in Computer Science at Princeton University as an NSF Graduate Research Fellow, Gordon Wu scholar, and Siebel scholar.