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Patrick Cahan, M.S., Ph.D.
Assistant Professor of Biomedical Engineering
Research Interests: Stem Cell Engineering; Computational Biology; Single Cell Genomics
Patrick Cahan grew up in St. Louis, MO. He received a B.S. in Computer Science from University of Maryland, Baltimore County, where he got his first research experience working on Information Retrieval systems. While working in Chile as a programmer, Patrick became interested in Computational Biology and returned to the US to pursue a M.S. in Genomics and Bioinformatics at GWU in Washington D.C., and then a Ph.D. in Computational Biology from Washington University in St. Louis, where he investigated the impact of DNA copy number variation on gene expression in Dr. Timothy Graubert's lab. His postdoctoral work in the lab of Dr. George Daley at Harvard Medical School and Boston Children’s Hospital included the development and experimental validation of computational methods to assess and improve engineered cells, such as those resulting from directed differentiation of pluripotent stem cells.
- Assistant Professor of Biomedical Engineering
Departments / Divisions
- B.S., University of Maryland (Baltimore County) (Maryland) (2000)
- M.S., George Washington University (District of Columbia) (2004)
- Ph.D., Washington University in St. Louis (Missouri) (2009)
Research & Publications
Gene regulatory networks (GRNs) govern the cell’s transcriptional output both at steady state and in response to perturbations, and thus act as major molecular determinants of cell-type identity. The long-term aims of the Cahan Laboratory are
To develop computational and experimental tools to map mammalian GRNs
To better understand how canonical signaling pathways modulate and are modulated by transiently established GRNs in the developing embryo
To characterize how cell type specific GRNs are rewired during tumorigenesis and progression
Towards these ends, we work across several disciplines including molecular and developmental biology, manipulation of pluripotent stem cells, population based and single-cell genomics, and computational and network biology. The outcomes of this research program will include improving the fidelity of directed differentiation to mesendodermal lineages (for purposes of disease modeling, drug screening, and regenerative medicine), the generation of fundamental insights into the interactions between GRNs, signaling pathways, and cell fate decisions, and improved models of human tumors.