Carnegie-Mellon University is awarded a grant to model dynamic systems in the cell. Gene expression is a temporal process and most systems in the cell are continuously regulated. New high-throughput biological techniques can generate large quantities of static and time series data. Time series experiments can be used to identify pathways and networks that are activated as a part of a biological system or in response to external stimulus. These methods hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, time series datasets also introduce many new computational challenges. In this project algorithms specifically designed for such experiments will be developed to take advantage of unique features in such data and address the unique problems they raise. This will be achieved by developing computational methods for the analysis of gene expression data and by combining different data sources using a variety of computational techniques. The educational component of this proposal will develop ways to teach and supervise computer science, biology and medical students by developing new classes at the graduate and undergraduate levels, by fostering close interactions with biologists in both research and training, and by helping develop and execute a joint PhD program in computational biology between Carnegie Mellon University and the University of Pittsburgh.
