Computational and Systems Biology
GRE is optional for this program
The goal of the Computational and Systems Biology Program is to train the next generation of scientists in technology intensive, quantitative, systems level approaches to molecular biology. We aim to graduate students who are as comfortable operating the latest high end instrumentation as they are manipulating the mathematical formalisms that are required to make sense of their data. It is our hope that the students who join the Computational and Systems Biology Program will apply these approaches to unraveling the complex genetic circuits that control the cell and statistical approaches to understanding the genetics of human disease.
Technological advances are having a major impact on molecular biology. Advances in experimental techniques mean that large amounts of sequence, expression, and localization data are now routinely gathered by individual investigators. In addition terabytes of these kinds of data are stored in various public and private databases. Concurrently, access to large scale computing resources has become more and more common in molecular biology laboratories. Students in the Computational and Systems Biology Program will learn to leverage these advances in both experimental and computational resources.
Faculty in the Computational and Systems Biology Program work on a variety of different biological problems, but in most cases students will find a tight coupling between computational and experimental approaches. Some of the general areas in which faculty work include:
- Large-scale genetic network analysis and reconstruction
- Technology development for high-throughput collection of genetic and biochemical data
- Molecular modeling of genetic regulatory circuits
- Real time, single cell analyses of genetic regulatory circuits
- Specificity and evolution of DNA-protein interactions
- Algorithm development for comparison of DNA, RNA, and protein sequences
- Synthetic Biology Complex trait analysis
- Population genetic analysis of genetic variation
- Functional genomic approaches to disease gene identification
