Fallahi-Sichani, Mohammad
Primary Appointment
Associate Professor, Biomedical Engineering, Biomedical Engineering
Education
- PhD, Chemical Engineering, University of Michigan
- Postdoc, Systems Biology, Harvard Medical School
Contact Information
MR5 2215
Box 800759, Health System
Charlottesville, Virginia 22908
Telephone: 434-924-9950
Email: fallahi@virginia.edu
Website: https://www.fallahi-sichani-lab.com/
Research Disciplines
Biochemistry, Biomedical Engineering, Biotechnology, Cancer Biology, Cell and Developmental Biology, Computational Biology, Epigenetics, Experimental Pathology, Metabolism, Molecular Biology, Molecular Pharmacology, Molecular Physiology and Biological Physics, Translational Science
Research Interests
Cancer systems biology, Single-cell quantitative biology, Computational modeling
Research Description
The systems biology research in the Fallahi-Sichani lab is focused primarily on understanding the mechanisms through which human cancer cells respond heterogeneously to environmental and therapeutic perturbations. These responses can take the form of changes in gene expression, metabolic state, or cell fate decisions such as differentiation, cell division, growth arrest, or induction of cell death in response to cancer drugs. Interestingly, these responses vary among cells in different states, even those that are genetically identical. Understanding the mechanisms that underpin heterogeneous cell fate decisions and cellular plasticity has been a key challenge for quantitative biology and precision medicine. To address these challenges, we deploy cutting-edge, high-throughput, highly multiplexed technologies to generate hypothesis-driven datasets of single-cell behaviors, apply modern multivariate computational tools to analyze such high-dimensional datasets, and create quantitative models to describe tumor cell behaviors at single-cell, molecular and network levels. The iterative use of experimental and modeling methods enables us to discover novel mechanisms of cellular signaling, plasticity and heterogeneity in cancer cell state and drug response, validate these mechanisms, and utilize them to guide improvement in cancer therapies.