Saptarshi Sinha, Ph.D.
Systems Biology (Department of Physics; The Bose Institute, Kolkata, India)
Computational and Systems Biology (Postdoctoral fellowship, UC San Diego)
AAI Intersect fellow in Computational Immunology (2023)
Interim Director, Center for PreCSN
"In 1911 the U.S. naturalist John Muir penned the following words: "When we try to pick out anything by itself, we find it hitched to everything else in the universe".
This central concept, i.e., everything is connected, is what has fueled my interest in modeling complex and dynamic biological phenomena and visualizing them as actionable networks, stoked my interest in big-data science, and inspired me to pursue a career in systems biology. These crosscutting concepts in science, i.e., "big-data" and "systems", not only serve as unifying paradigms for modern biology, but also show powerful synergy when combined. While big-data science is great at rapidly idenitifying patterns that the human eye may not recognize, systems thinking can unearth powerful and complex insights at a slower pace; when combined, they can rapidly generate insight from information.
Big-data science and systems are also the unifying aspect of my career and contributions to date, which have used diverse biological networks, from atom-atom contact in proteins, to protein-protein and gene-gene regulatory networks within cells, to the use of network approaches for the revelation of insights (e.g., clinically useful biomarkers or therapeutics targets) that could impact how we treat patients. Here, with the help of big-data-derived complex models, we study the fascinating realm of emerging properties within biological systems, seeking to understand how the interactions of individual components give rise to complex behaviors and functions.
As PreCSN's interim Director, I create and nurture the intense, vibrant, and rich hybrid environment of both high throughput big-data computation and systems-level thinking that is geared to support several transdisciplinary programs at the Institute for Network Medicine, each representing some of the most wicked problems in biology and medicine."