CAREER: Modeling Dynamic Systems in the Cell

卡内基-梅隆大学被授予对细胞中的动态系统进行建模的拨款。基因表达是一个时间过程，细胞中的大多数系统都受到持续的调控。新的高通量生物技术可以产生大量的静态和时间序列数据。时间序列实验可以用来确定作为生物系统的一部分或对外部刺激做出反应而被激活的路径和网络。这些方法通过提供对细胞活动的大规模观察，有望给分子生物学带来革命性的变化。然而，时间序列数据集也带来了许多新的计算挑战。在这个项目中，将开发专门为这些实验设计的算法，以利用这些数据中的独特特征，并解决它们所提出的独特问题。这将通过开发用于分析基因表达数据的计算方法以及通过使用各种计算技术组合不同的数据源来实现。这项提案的教育部分将开发教授和监督计算机科学、生物学和医学生的方法，方法是在研究生和本科生水平上开设新的课程，促进与生物学家在研究和培训方面的密切互动，并帮助开发和执行卡内基梅隆大学和匹兹堡大学联合开展的计算生物学博士项目。

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.