Systems Biology of Disease and Therapeutics (SBDT)
The research programs within the Systems Biology of Disease and Therapeutics (SBDT) multidisciplinary training area share the following common theme. Knowledge of the health or disease state of a cell, tissue, or organism, requires an understanding of networks of molecular interactions within and between cells. Thus, new and more effective therapies can be developed by considering how biological components interact with each other to produce emergent behaviors.
Accordingly the Systems Biology of Disease and Therapeutics curriculum emphasizes training in several disciplines and approaches ranging from molecular and cell biology to genetics and biochemistry, from physiology and pharmacology to neuroscience and computational biology, from single cell model systems to organ-level and animal studies. The Systems Biology of Disease and Therapeutics faculty investigate disease processes and drug actions in a variety of cell types but share the underlying philosophy that systems approaches are required for transformative advances. Because mathematics provides a common language for understanding physical and biological processes, quantitative reasoning and computational approaches are integrated into the curriculum and employed by many of the faculty. The core courses, journal clubs, lab rotations, and works-in-progress presentations provide our students with an understanding of how to use diverse data sets to delineate biological networks, how to translate this information into new therapeutic and preventive strategies, and how to apply this paradigm to their own research.
Jeanne Hirsch, PhD is Associate Professor of Pharmacology and Systems Therapeutics. Her laboratory studies signal transduction pathways that are mediated by heterotrimeric G proteins in the yeast Saccharomyces cerevisiae.
Eric Sobie, PhD is Associate Professor of Pharmacology and Systems Therapeutics, and a member of the Systems Biology Center of New York (SBCNY). His research focuses on gaining quantitative understanding of normal and pathological heart function through the coupling of experimental measurements with mathematical modeling.
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