Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented detailed information about biological systems that are transforming the way that we do almost all of biology. Problems investigated by computational biologists span a wide spectrum including topics as diverse as the genetics of disease susceptibility, comparing whole DNA genomes to uncover the secrets of evolution, predicting protein structures and understanding their motions and interactions, designing new therapeutic drugs, mathematically modeling the complex signaling mechanisms within cells, predicting how ecosystems will respond to climate changes, and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics and the physical sciences as well as in biology. A key goal in training is to develop the ability to relate biological processes to mathematical models that can be solved computationally.

Cornell faculty work primarily in four subareas of computational biology: biomolecular structure, bioinformatics and data mining, ecology and evolutionary biology, and statistical and computational methods for modeling biological systems. These include the computational study of topics such as DNA databases, protein structure and function, computational neuroscience, biomechanics, population genetics, and management of natural and agricultural systems. Beyond the basic core skills in mathematics, physical sciences and biology, the computational biology program of study requires additional coursework in mathematics, 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 will be studied.

Students should enroll in the more rigorous courses in the physical sciences and be well prepared in mathematics. Depending on student interest, students may wish to pursue additional courses in these areas. Computational biology is a new emerging area that has applications as broad as biology itself. The problems of interest, as well as the tools available to study them, will undoubtedly change during the four years of an undergraduate program: So students are encouraged to gain fundamental skills and understanding that will allow them to focus on specific subareas and problems later in their careers. The program is an excellent preparation for students who wish to specialize in one of these computational areas in graduate school. 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.