Marisa Eisenberg is an assistant professor in the Department of Epidemiology, and in the Department of Mathematics. Her research revolves around mathematical epidemiology, focus on using and developing parameter estimation and identifiability techniques to model disease dynamics. Her group builds multi-scale models of infectious disease, including HPV, cholera and other environmentally driven diseases.
Denise Kirschner is a Professor in the Department of Microbiology and Immunology. She serves as founding co-director of the Center for Systems Biology, is affiliated with both the Center for the Study of Complex Systems and the Center for Computational Medicine and Bioinformatics. Her research involves the modeling of immunological responses in infectious diseases, focusing on questions related to host-pathogen interactions. The pathogens she studies include both bacteria (Mycobacterium tuberculosis) and HIV-1. Such pathogens have evolved strategies to evade or circumvent the host-immune response and the lab’s goal is to understand the complex dynamics involved and develop optimal treatment and vaccine strategies.
Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.
Santiago Schnell’s lab combines chemical kinetics, molecular modeling, biochemical measurements and computational modeling to build a comprehensive understanding of proteostasis and protein forlding diseases. They also investigate other complex physiological systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole.
The Linderman group works in the area of computational biology, especially in developing multi-scale models that link molecular, cellular and tissue level events. Current areas of focus include: (1) hybrid multi-scale agent-based modeling to simulate the immune response to Mycobacterium tuberculosis and identify potential therapies, (2) models of signal transduction, particularly for G-protein coupled receptors, and (3) multi-scale agent-based models of cancer cell chemotaxis.
His research is in computational physics, specifically biophysics (tumor growth and cell mechanics) and materials physics (battery materials, structural alloys and semiconductor materials). In these areas Garikipati’s group focuses on developing mathematical and numerical models of phenomena that can be described by continuum analyses that translate to PDEs. Usually, these are nonlinear, and feature coupled physics, for example chemo-thermo-mechanics. Our numerical techniques are mesh-based variational methods such as the finite element method and its many variants. In some problems we make connections with fine-grained models, in which case we work with kinetic Monte Carlo, molecular dynamics or electronic structure calculations in some form. In the realm of analysis, we often examine the asymptotic limits of our mathematical models, and the consistency, stability and convergence of our numerical methods.