Mohammad Fallahi-Sichani, Ph.D.

Merck Fellow of the Life Sciences Research Foundation
Harvard Medical School
Department of Systems Biology

Email:
fallahi [AT] umich.edu
mohammad_fallahisichani [AT] hms.harvard.edu



Mohammad Fallahi-Sichani, Ph.D.

ABOUT ME:
I am a Life Sciences Research Foundation (LSRF) Postdoctoral Fellow in the Systems Biology department at Harvard Medical School, working with Peter Sorger. I received my Ph.D. in Chemical Engineering from the University of Michigan, Ann Arbor in April 2012 under the supervision of Jennifer Linderman and Denise Kirschner. During my Ph.D, using multi-scale modeling approaches together with experiments on mouse models of tuberculosis, I investigated the molecular and cellular mechanisms associated with TNF signaling that are involved in immunity to M. tuberculosis and may be new targets for therapy. My doctoral thesis won the 2011 Richard and Eleanor Towner Award for Outstanding Ph.D. Research from the University of Michigan. I joined the Sorger lab with an interest in applying my background in engineering and multi-scale processes to study how response of cancer cells to targeted therapeutics are determined at the level of cell signaling networks.

RESEARCH INTERESTS:
I am currently using various computational and experimental approaches to develop a mechanistic and quantitative understanding of the often-discussed but poorly understood phenomenon of "oncogene addiction" in melanoma. I am particularly interested in understanding of how inhibition of constitutively activated BRAF kinase induces intrinsic apoptosis in BRAF(V600E) melanoma cells. Such understanding will be critical for predicting response of melanoma cancer cells to therapy and ultimately improving drug sensitivity in resistant tumors.

The underlying hypothesis for my work is: (1) a common set of critical proteins within the intracellular signaling and apoptotic networks of diverse melanoma cells regulate apoptosis in response to BRAF inhibition but the rate-limiting players differ from one tumor to the next, and even one cell to the next, making it difficult to develop a consistent biochemical picture; (2) mathematical models describing dynamic, multi-factorial differences in signaling pathways among different cell lines can capture these differences and reveal how genetically diverse tumors canalized into a few distinct network states.