Jayajit Das, PhD
Dr. Das is a PhD and principal investigator for the center. Jayajit’s lab uses theoretical and computational approaches based on statistical physics to uncover basic mechanistic principles underlying our innate and adaptive immune response. Obtaining such mechanistic principles from experimental observations alone is often difficult because the pertinent processes include co-operative dynamic events with many participating components. A further complication that confounds intuition is stochastic fluctuations in these systems with small numbers of molecules. However, by synergistically integrating observations from experiments with transgenic animals, single molecule techniques and imaging studies probing molecular events in live animals with these theoretical and computational approaches we can provide system-level understanding into such complex systems. The mechanistic insight gained from such studies not only will help develop future experiments to unravel basic principles of our immune system, but may also help envision therapeutic strategies for infectious disease and autoimmune disorders.
Darren Wethington, BS
Darren is a research aide for the Das Lab in the Battelle Center for Mathematical Medicine. He graduated from The Ohio State University in 2016 with a BS in Chemical and Biomolecular Engineering.
He assists Dr. Das in creating and running computer programs to analyze mass cytometry data on the activation of NK cells using Monte Carlo methods and simulated annealing. He creates and tests an effective in silico model, and hopes to better understand the mechanism by which NK cells are activated.
Darren likes playing drums, guitar and bass in his free time. Bowling and watching football are also some of his hobbies.
Jonathan earned his PhD in Materials Engineering from the New Mexico Institute of Mining and Technology. His research is focused on computational modeling of biofilms formed by nontypeable Haemophilus influezae (NTHI) bacteria, which play an important role in a number of respiratory tract diseases, including chronic ear infections in children. We aim to better understand the structure and formation of these biofilms by developing a computational model based on statistical physics and validating by closely comparing computational results with experimental images.