Journal of Computational Systems Biology

Jason W. Locasale, Ph.D. is an Assistant Professor in the Division of Nutritional Sciences at Cornell University. He graduated from Rutgers University, Summa Cum Laude with a dual degree in Chemistry and Physics. He received his Ph.D. at the Massachusetts Institute of Technology in Biological Engineering. He then studied cancer metabolism at Harvard Medical School where he worked as an American Cancer Society postdoctoral fellow and then later as an Instructor on the faculty.

Dr. Locasale is a recipient of the NIH Pathway to Independence Award, the Ruth Kirchstein National Service Award, the Benjamin Trump Award for Excellence in Cancer Research from the Aspen Cancer Society, and was elected as a Junior Fellow at the Bert and N. Kuggee Vallee Foundation. Dr. Locasale serves on the advisory board of the American Association of Cancer Research - Chemistry in Cancer section. He has authored over 35 publications and has co-authored numerous textbooks and patents.

Dr. Locasale’s research focuses on understanding metabolism in cell growth, cancer pathogenesis and therapeutic intervention. His efforts have focused on understanding the Warburg Effect – the observation that tumor cells process glucose through fermentation even when oxygen is abundant for respiration. He has defined the mechanistic principles that lead to the Warburg Effect and is now investigating its downstream consequences on cellular physiology. He is currently translating this knowledge to develop biomarkers of agents that affect glucose metabolism in cancer. As a postdoctoral fellow, Dr. Locasale made the seminal discovery that a major pathway utilized by glucose-metabolizing cancer cells involved the diversion of glycolytic flux into one-carbon metabolism through de novo serine and glycine metabolism. Dr. Locasale is currently pursuing the role this pathway in disease pathogenesis and cell transformation and this work has led him to study the interplay between metabolism, signal transduction, and epigenetics. At the core of this effort lies the utilization of computational modeling and mass spectrometry-based metabolomics. Together this systems biology approach combines these tools with an integration of genetics, biochemistry, and cell biology.