Cornell University

Computation has become essential to biological research. Technology for collecting high-throughput data, such as genomic technology, mass spectrometry, and MRI imaging, and the development of large-scale databases, such as those for genomes, epidemiology, and compilations of biological information types, have made available unprecedented amounts of detailed information that require computationally intensive methodologies to access and analyze. These data and computational methods are transforming almost all of biological research.

Problems investigated by computational biologists include topics as diverse as the genetics of disease susceptibility; comparing entire genomes to reveal the evolutionary history of life; predicting the structure, motions, and interactions of proteins; designing new therapeutic drugs; modeling the complex signaling mechanisms within cells; predicting how ecosystems will respond to climate change; and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics, machine learning, and the physical sciences as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models. Cornell faculty work primarily in six subareas of computational biology: 1. computational and statistical genomics, 2. population, comparative, and functional genomics, 3. bioinformatics, 4. proteomics, 5. ecology and evolutionary biology, and 6. statistical and computational methods for modeling biological systems.

Beyond core skills in mathematics, physical sciences and biology, the computational biology concentration requires additional coursework in mathematics and computer programming, a “bridging” course aimed at connecting biology to computation, and an advanced course where the theoretical/computational component of one aspect of biology is studied. Students should enroll in the more rigorous courses in the physical and mathematical sciences, and may wish to take additional courses in these areas.

Computational biology has applications as broad as biology itself. The problems of interest and the tools available to study them are constantly evolving, so students are encouraged to gain fundamental skills that will serve them throughout their careers. There is great, and increasing, demand for research scientists and technical personnel who can bring mathematical and computational skills to the study of biological problems. This concentration is also an excellent preparation for graduate study in any area of biology or computational biology.