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.
Barry Grant is an Assistant Professor in the Department of Computational Medicine & Bioinformatics. The Grant Lab uses computational approaches, based on both biophysics and bioinformatics, to study the structure, function and evolution of biological macromolecules. We are particularly interested in nature’s nanomachines: molecular motors and switches, which lie at the heart of biological processes, from the division and growth of cells to the muscular movement of organisms. A major portion of our research is focused on deciphering how these fascinating proteins work, and how to manipulate them for industrial and medical advantage.
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.
Prof. Radev has a joint appointment in Electrical Engineering and Computer Science in the College of Engineering, and the School of Information.
His areas of research are natural language processing and information retrieval, especially scalable processing of large-scale textual data sets. He has worked on text summarization, question answering, semantic similarity, social network analysis, survey generation, citation prediction, topic modeling in political science, biomedical language processing, and random walks for text analysis.
Aaron A. King is an Associate Professor of Ecology & Evolutionary Biology, and is affiliated with the Department of Mathematics, the Center for the Study of Complex Systems, the Center for Computational Medicine & Bioinformatics, the Fogarty International Center, and the National Institutes of Health. Prof. King develops and applies computationally intensive methods for using stochastic dynamical systems models to learn about infectious disease ecology and epidemiology. These systems are typically highly noisy and nonlinear and are frequently uncomfortably high-dimensional. Nevertheless, the King group’s approaches allow them to find out what the data have to say about the mechanisms that generate them.
David Sept is a Professor in the Department of Biomedical Engineering, and he is affiliated with the Center for Computational Medicine and Bioinformatics. The Sept lab works in the area of computational biology and we use a wide array of computational techniques to study protein, drug and cellular systems. In addition to “standard” simulation techniques like molecular dynamics, we are developing new simulation and analysis methods for application in more complex systems.
His research goal is to develop accurate and efficient numerical methods for computational problems in science and engineering. The methods he works on typically use the Green’s function to convert the relevant differential equation into an integral equation. Krasny develops treecode algorithms for efficient computation of long-range particle interactions. Topics of interest include fluid dynamics (vortex sheets, vortex rings, Hamiltonian chaos, geophysical flow), and electrostatics (Poisson-Boltzmann model for solvated proteins). He is also interested in modeling charge transport in organic solar cells.
Ronald Larson is the A.H. White and G.G. Brown Professor of Chemical Engineering. He is affiliated with the departments of Chemical Engineering, Macromolecular Science, Biomedical Engineering, and Mechanical Engineering. He currently serves as interim Chair of Biomedical Engineering. Larson’s research interests include theory and simulations of the structure and flow properties of viscous or elastic fluids, sometimes called “complex fluids,” which include polymers, colloids, surfactant-containing fluids, liquid crystals, and biological macromolecules such as DNA, proteins, and lipid membranes. He also studies computational fluid mechanics, including microfluidics, and transport modeling, using mesoscopic and macroscopic simulation methods. He has written numerous scientific papers and two books on these subjects, including a 1998 textbook, “The Structure and Rheology of Complex Fluids.”
Charles L. Brooks III is the Warner-Lambert/Parke-Davis Professor of Chemistry and a Professor of Biophysics. He is affiliated with the department of Chemistry, Biophysics Program, program in Applied Physics, Molecular Biophysics Training Program (Director), program in Chemical Biology, Bioinformatics Graduate Program, Center for Computational Medicine and Bioinformatics and the Medicinal Chemistry Interdepartmental Graduate Program. The research in the group of Charles L. Brooks III is focused on the application of statistical mechanics, quantum chemistry and computational methods to chemically and physically oriented problems in biology. The group develops and applies computational models to studies of the dynamics of proteins, nucleic acids and their complexes, including virus structure and assembly. They specifically develop novel computational methods for the inclusion of pH effects in modeling biological systems. Significant focus is in the development of a large, world-wide distributed software package for molecular simulations, CHARMM. Efforts are ongoing to explore new means of parallel and accelerated computation utilizing scalable parallel algorithms for molecular dynamics and integrated CPU/GPU computational models.