Christina V Theodoris, MD, PhD
Christina Theodoris is an Assistant Investigator in the Gladstone Institute of Cardiovascular Disease and Gladstone Institute of Data Science and Biotechnology. She is also an Assistant Professor in the Department of Pediatrics at the University of California, San Francisco (UCSF). She completed her B.S. in Biology at California Institute of Technology, where she worked in the Eric Davidson Lab studying gene regulatory networks in early sea urchin development. She then completed her MD/PhD in Developmental and Stem Cell Biology at UCSF. During her graduate work in Deepak Srivastava’s lab at Gladstone, co-mentored by Katherine Pollard and Benoit Bruneau, she developed an innovative network-based approach to therapeutic design leveraging machine learning and iPS cell disease modeling, which ultimately identified a candidate molecule for the treatment and prevention of cardiac valve disease currently under further development toward clinical trials.
As a postdoctoral fellow in the Department of Data Science at Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard, co-mentored by X. Shirley Liu and Patrick Ellinor, she developed a novel deep learning model leveraging large-scale single cell transcriptomic data to enable context-specific predictions in settings with limited data in network biology through transfer learning. She also co-developed a machine learning methodology that systematically contrasts single-cell multimodal transcriptomic and chromatin accessibility data to infer the regulatory circuitry driving fate decisions within cell state trajectories. She completed her medical subspecialty training in pediatrics and medical genetics at Boston Children’s Hospital, and her clinical experiences in pediatric cardiovascular genetics inform and direct her research program.
Christina Theodoris now leads her research group at the Gladstone Institutes and UCSF leveraging machine learning and experimental genomics to map the gene regulatory networks disrupted in cardiovascular disease to develop network-correcting therapies.