"As a student in the CBB program and the Integrated Graduate Program in Physical and Engineering Biology (PEB), I have seen firsthand the interdisciplinary opportunities available at Yale. I've noticed that even if students come in thinking they have a pretty good idea of what they want to do, a lot of them tend to expand their scope of intellectual pursuit and rethink their initial goals. I think that's only natural given how much more informed we become as we make it through our first year. What I really appreciate about my program, and BBS in general, is that we have plenty of opportunities and encouragement to explore different areas of interest regardless of our level of understanding in a particular field. As someone who started off with a lot of uncertainty, I now have a much better sense of what I want as my long term goal."
View the 2019 CBB Track open house webinar to hear from Track faculty and students.
The past few years have witnessed a revolution in the biological sciences. Exciting and efficient new approaches have become available for the analysis of entire genomes (the complete genetic program of an organism) and proteomes (the entire set of proteins encoded by an organism), and the analysis of large data sets. A critical direction of future biological research will be to determine the function of the many genes identified by the genome analysis of these different organisms, how the many different genes are regulated, and how they work together to mediate complex biological processes at the molecular level. In particular, the systematic acquisition of data made possible by genomics technologies has created a tremendous gap between available data and their biological interpretation. Given the rate of data generation, it is well recognized that this gap will not be closed with direct individual experimentation. Computational and theoretical approaches to understanding biological systems provide a key inroad into closing this gap. Computational Biology and Bioinformatics is a new field where biological problems are addressed using these data with computational, theoretical, and genomics techniques. Activities in this field include: biological modeling, genomic analysis, database and data-mining, algorithm development and high-performance computing, statistical and mathematical analyses, as well as computational management of large-scale projects.