DeniseKirschner

Denise Kirschner

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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.

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Daniel Forger

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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.

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Barry Grant

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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.

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Jennifer Linderman

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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.

Hybrid multi-scale model of the immune response to Myobacterium tuberculosis in the lung. Selected immune cell behaviors and interactions captured by the model are shown. Not shown are single cell receptor/ligand dynamics involving the pro-inflammatory cytokine tumor necrosis factor (TNF) and the anti-inflammatory cytokine interleukin 10 (IL-10).

Hybrid multi-scale model of the immune response to Myobacterium tuberculosis in the lung. Selected immune cell behaviors and interactions captured by the model are shown. Not shown are single cell receptor/ligand dynamics involving the pro-inflammatory cytokine tumor necrosis factor (TNF) and the anti-inflammatory cytokine interleukin 10 (IL-10).

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Dragomir Radev

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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.

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Aaron King

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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.

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David Sept

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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.

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Robert Krasny

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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.

This picture illustrates the instability of a vortex ring. The ring was modeled as a circular disk vortex sheet with an imposed perturbation of azimuthal wavenumber m=8. The ring’s motion was computed using a Lagrangian particle method and a treecode algorithm for fast evaluation of the induced velocity. The picture shows three isosurfaces of vorticity at a late time in the simulation. The results reveal details of the instability, in particular the relation between axial flow and collapse of the vortex core.

This picture illustrates the instability of a vortex ring. The ring was modeled as a circular disk vortex sheet with an imposed perturbation of azimuthal wavenumber m=8. The ring’s motion was computed using a Lagrangian particle method and a treecode algorithm for fast evaluation of the induced velocity. The picture shows three isosurfaces of vorticity at a late time in the simulation. The results reveal details of the instability, in particular the relation between axial flow and collapse of the vortex core.

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Ronald Larson

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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.”

Simulated three dimensional self assembly of spherical “Janus” particles with attractive faces (blue, on far left and red on far right) and non-attractive faces (white). The far left shows packing in the “rotator” phase, where the attractive faces have not ordered orientationally, which occurs at lower temperature. Other images show single sphere, or groups of spheres, indicating hexagonal ordering. Surrounding points show positions of surrounding spheres, at multiple time points, indicating motions about crystal lattice points.

Simulated three dimensional self assembly of spherical “Janus” particles with attractive faces (blue, on far left and red on far right) and non-attractive faces (white). The far left shows packing in the “rotator” phase, where the attractive faces have not ordered orientationally, which occurs at lower temperature. Other images show single sphere, or groups of spheres, indicating hexagonal ordering. Surrounding points show positions of surrounding spheres, at multiple time points, indicating motions about crystal lattice points.

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Sharon Glotzer

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Sharon Glotzer is a Professor of Chemical Engineering and of Material Science and Engineering. The Glotzer group uses computer simulations to discover the fundamental principles by which nanoscale systems of building blocks self-assemble into higher order, complex, and often hierarchical structures. Their goal is to learn how to manipulate matter at the molecular, nanoparticle, and colloidal scales to create “designer” structures through assembly engineering. Using molecular dynamics and Monte Carlo simulation codes developed in-house for graphics processors (GPUs) and scalable to large hybrid CPU/GPU clusters, they are the leading computational assembly group in the world, with the most powerful codes for studying assembly and packing. Among others, they are the lead developer of HOOMD-Blue, the fastest molecular dynamics code written solely for GPUs and distributed freely as open source software on codeblue.umich.edu.  Based on the fundament scientific principles of assembly gleaned from their studies, they carry out high throughout simulations for materials by design, contributing to the national Materials Genome Initiative.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.