NanoString Technologies

Harvard Medical School

Peter Sorger, Ph.D is a Professor of Systems Biology at Harvard Medical School and holds a joint appointment in MIT’s Dept. of Biological Engineering and Center for Cancer Research. He received his A.B. from Harvard College, Ph.D. from Trinity College Cambridge, U.K. and trained as a postdoctoral fellow with Harold Varmus and Andrew Murray at the University of California, San Francisco. Sorger was co-founder of the MIT systems biology program CSBi, Merrimack Pharmaceuticals and Glencoe Software and serves on the scientific advisory and corporate boards of several other technology companies. He is currently Chair of the CSF study section of the NIH and Director of the NIH-NIGMS CDP Center for Systems Biology.

Sorger's lab consists of 26 graduate students, postdoctoral fellows and staff scientists is involved in both computational and experimental biology. One of the long term goals of lab’s research are to identify molecular lesions that cause genomic instability and promote tumorigenesis, to determine their frequency in normal and cancerous cells and to develop improved means to kill diseased tissues. When healthy cells divide, chromosomes are partitioned among daughter cells with great fidelity. However, in cancer cells, this fidelity is lost and cells exhibit genomic instability. It is thought that genomic instability plays an important role in accumulation of the multiplicity of genetic mutations characterizing cancer in humans. Approaches in the lab include quantitative single-cell measurement, genetically engineered mice and quantitative cell biology.

A second goal in the Sorger Lab is to understand the pathways of mammalian cell signaling from a systems – rather than a component by component – perspective. Mathematical and experimental analysis of primary and transformed human cells are used to elucidate the biochemical circuits by which the insulin-like and epidermal growth factors, and the TRAIL and TNF death ligands, exert opposing effects on cell survival. The lab is particularly interested in elucidating differences between normal and diseased cells with respect to their responsiveness to anti-cancer drugs. Towards this end, we analyze large sets of protein-based data using both statistical and mechanistic mathematical modeling.