Mount Sinai Institute for Systems Biomedicine

About Us

The Mount Sinai Institute for Systems Biomedicine (MSISB) is committed to spearheading transdisciplinary approaches to both basic and translational research. In doing so, we are contributing significantly to the discipline of precision medicine.

Integrating cell biology, human physiology, and pathophysiology with big data, MSISB is forever changing the dynamics of health record databases. Researchers in the institute use machine learning techniques and computational modeling. This includes graph theory and differential equation-based dynamical models experiments in induced pluripotent stem-cell-derived cells from various tissues and in animal models, and mechanism based clinical trials. Through these resources, we are striving to develop a deep understanding of systems physiology, disease mechanisms, and drug actions. Such understanding can help us develop computational diagnostic programs for clinical decision support and novel therapeutics.

Our research foci are anchored by two grants from National Institutes of Health funded centers, the Systems Biology Center from NIGMS and the LINCS Drug Toxicity Signature Center Grant from NHGRI and the NIH Common Fund. Utilizing these center grants, MSISB is developing a research infrastructure that enables us to perform computation for large scale dynamical modeling and high throughput gathering of data on cell biological and physiological processes in human cells. MSISB is catalyzing research with an eye on developing and implementing large scale dynamical and network models to study physiology and pathophysiology so that we can formulate novel treatment regimens and systems pharmacology. We call on principles from physics and chemistry to drive predictive modeling of dynamic processes in human health and disease.

MSISB fosters the development of convergent research teams that form seamless collaborations between disciplines that meld depth and excellence in different disciplines into identifiable and actionable outputs whose value will be recognized by clinicians.