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.

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.

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).

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.

king-image

 

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.

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.

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.

Charles Brooks

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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.

Gregory J. Dick

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Microbial communities host incredible biological diversity that is encoded at the genomic level. The recent application of high-throughput DNA-sequencing technologies to these communities provides exciting new insights into uncultured organisms while presenting new computational challenges associated with massive and multidimensional data. Professor Dick’s laboratory utilizes such metagenomic and metatranscriptomic approaches to problems in environmental and geological science, addressing questions such as: How do microorganisms control elemental cycles in deep-sea hydrothermal systems?  How did microbial processes influence the timing of Earth’s oxygenation? Computational applications focus on metagenomic and metatranscriptomic assembly, comparative genomics, and “binning,” whereby fragmentary metagenomic sequences of unknown origin are assigned to organisms on the basis of genome signatures of nucleotide composition.

An emergent self-organizing map of genome signatures (tetranucleotide frequency) from a microbial community at the Mid-Cayman Rise (5,000 meters water depth, Caribbean). This approach allows computational reconstruction of genome sequences from complex microbial communities.

An emergent self-organizing map of genome signatures (tetranucleotide frequency) from a microbial community at the Mid-Cayman Rise (5,000 meters water depth, Caribbean). This approach allows computational reconstruction of genome sequences from complex microbial communities.

Krishna Garikipati

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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.

Isogeometric analysis (weak form based-solution of PDEs with spline basis functions) of phase transformations in a battery material.

Isogeometric analysis (weak form based-solution of PDEs with spline basis functions) of phase transformations in a battery material.