The Computational Biology Ph.D. program is training the next generation of Computational Scientists to tackle research using the big genomic, image, remote sensing, clinical, and real world data that are transforming the biological sciences.
The graduate field of Computational Biology offers Ph.D. degrees in the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological systems.
Computation has become essential to biological research. Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of biology. The computational biologist must have skills in mathematics and computation as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models.
The field provides interdisciplinary training and research opportunities in a range of subareas of computational biology including comparative and functional genomics, systems biology, molecular and protein networks, population genomics and genetics, bioinformatics, model system genomics, agricultural genomics, and medical genomics.
Students majoring in computational biology are expected to obtain a broad, interdisciplinary knowledge of fundamental principles in biology, computational science, and mathematics. But because the field covers a wide range of areas, it would be unrealistic to expect a student to master each facet in detail. Instead, students choose from specific subareas of study: They are expected to develop competence in at least one specific subdomain of biology and in relevant subareas of computational science and mathematics.
