Computational biology and bioinformatics develop and apply techniques from applied mathematics, statistics, computer science, physics and chemistry to the study of biological problems, from molecular to macro-evolutionary. By drawing insights from biological systems, new directions in mathematics and other areas may emerge.
The Mathematics Department has led the development of advanced mathematical modeling techniques and sophisticated computational algorithms for challenging biological problems such as protein folding, biological network analysis and simulation of molecular machinery.
Mathematical modeling and computer algorithms have been extensively used to solve biological problems such as sequence alignment, gene finding, genome assembly, protein structure prediction, gene expression analysis and protein-protein interactions, and the modeling of evolution. As a result, researchers are now routinely using homology search tools for DNA/protein sequence analysis, genome assembly software for world-wide genome sequencing projects, and comparative genome analysis tools for the study of evolutionary history of various species. All of these widely used tools were developed, at least in part, by MIT Mathematics Department faculty, instructors and former students. Techniques and tools developed by computational biologists are widely used to drive drug development by pinpointing targets, screening molecules for biological activity, and designing synthetic molecules for specific uses.
Exciting problems in this field range include the protein folding challenge in bioinformatics and the elucidation of molecular interactions in the emerging area of systems biology. Mathematicians will likely make significant contributions to these fundamental problems.
Bonnie Berger Theoretical Computer Science, Computational Biological Modeling
Elchanan Mossel Probability, Algorithms and Inference
Instructors & Postdocs
Pui Tung Choi Applied and Computational Geometry, Metamaterials, Quantitative Biology, Medical Imaging
Christopher Rackauckas Scientific machine learning, physics-informed learning, numerical differential equations, systems pharmacology