Computational Biology is the newest of the department's four tracks for graduate education
and research. There are two faculty, David Green and Rob Rizzo who are biological
sciences PhDs using extensive mathematical modeling in their research. Carlos Simmerling
and Jin Wang in Chemistry are adjunct faculty who work closely with the Applied Math
computational biology group. A number of the faculty in the Computational Applied
Mathematics group have also worked on problems in biology, involving molecular dynamics,
organ modeling, and neural structure.
Evangelos Coutsias' research has focused on the modeling of nonlinear systems and continua, using techniques of applied mathematics on problems motivated from applied physics, engineering and biology. These include asymptotics and perturbation methods for the study of stability and bifurcation phenomena in plasma physics, biology and fluid mechanics; high accuracy numerical spectral methods for solving PDEs arising in continuum mechanics; and robust numerical methods for systems of multivariate polynomials for the solution of problems of inverse kinematics arising in molecular structure studies. His present work is on the development of computational methods for the study of protein structure, especially on the kinematic geometry of protein backbones subject to constraints. Current interests focus on the refinement of protein structure and the development of computational geometric methods for the efficient exploration of macromolecular shapespaces with application to protein design and drug discovery.
David Green's research is focused on computational studies of protein interactions. Key areas include: understanding the determinants of specificity in protein interactions through biomolecular simulation; development and application of algorithms for the design of binding interfaces; and development of tools for the study of protein-carbohydrate interactions, with a focus on the glycobiology of HIV-1 infection. His research combines techniques from applied mathematics and models from biophysical chemistry to solve important problems in biology and medicine. For more information, see Green Lab.
Dima Kozakov's research interests lie at the intersection of applied mathematics, physics and computational biology. He focuses on two main goals. The first is the development of mathematically elegant, computationally efficient and physically accurate algorithms for modeling macromolecular structure and function on the genome scale. The second is the application of novel methods to improving the understanding of biological problems and to the design of therapeutic molecules with desired biological and biomedical properties.
research focuses on Evolutionary Systems Biology and Computational Immunology, often in close collaboration with experimental biologists. In Computational Immunology a primary interest is computational modeling of antibody diversity generation, specifically somatic hypermutation in Immunoglobulin genes, which he is currently applying to the study of B-cell lymphomas such as chronic lymphocytic leukemia (CLL). In Evolutionary Systems Biology, Dr. MacCarthy uses computational models to investigate the evolution of robustness in cis-regulatory regions of gene network models and the high evolvability of sex determination gene networks. For more information, see MacCarthy Lab.
Robert Rizzo's research group seeks to understand the atomic basis for molecular recognition for specific biological systems involved in human disease such as HIV/AIDS, cancer, and influenza with the ultimate goal of developing new and improved drugs. Computational methods are used to model how molecules interact at the atomic level with a given drug target. The resultant 3D structural and energetic information is used to quantify and rationalize drug-binding for known systems and to make new predictions. For more information, see Rizzo Lab.