Mohammad Fallahi-Sichani, Ph.D.
Mohammad Fallahi-Sichani obtained his bachelor’s degree in Biotechnology from the University of Tehran in 2007. In 2012, he earned a Ph.D. in Chemical Engineering from the University of Michigan, where he was awarded a Rackham Predoctoral Fellowship and a Richard and Eleanor Towner Prize for Outstanding PhD Research. His PhD work co-mentored by Dr. Jennifer Linderman and Dr. Denise Kirschner combined multi-scale modeling approaches with wet-lab experiments to the study of mechanisms by which TNF signaling determines immunity to M. tuberculosis infection. He then joined the laboratory of Dr. Peter Sorger at Harvard Medical School as a Merck Fellow of the Life Sciences Research Foundation and was later awarded a K99 Pathway to Independence Award from the NCI. His postdoctoral research focused on the application of systems biology approaches to understanding the mechanisms of adaptive resistance and fractional response of cancer cells to anti-cancer drugs. In 2017, Mohammad joined the Department of Biomedical Engineering at the University of Michigan Medical School as an Assistant Professor. Research in his laboratory combines experimental and data-driven computational approaches with the goal of understanding oncogenic signaling and therapeutic mechanisms in human tumors.
Research in the Fallahi-Sichani lab is at the interface of bioengineering, cancer biology and quantitative pharmacology, and uses a combination of multiplex biochemical measurement, single-cell analysis, and multi-scale and network-level modeling to address a variety of challenges in the cancer biology area. In particular, we are interested in: (i) decoding signaling networks that enforce cancer cell fate decision in response to environmental and therapeutic perturbations and elucidating their differences with healthy cells, (ii) uncovering molecular origins of plasticity in cancer cell signaling that underlie heterogeneity in drug response among genetically diverse tumors or among genetically homogeneous tumor cells, and (iii) developing predictive computational models that guide novel approaches to enhance therapeutic response and overcome drug resistance, a major challenge facing cancer therapy.
Systems Biology, Cancer, Pharmacology, Signal Transduction, Epigenetics