CAREER: Modeling Dynamic Systems in the Cell

职业：细胞内动态系统建模

• 批准号: 0448453
• 负责人: Ziv Bar-Joseph
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0448453&HistoricalAwards=false
• 关键词: CAREER; Modeling; Dynamic; Systems; Cell

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.

卡耐基-梅隆大学获得了一项资助，用于模拟细胞中的动态系统。基因表达是一个暂时的过程，细胞中的大多数系统都是连续调节的。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。时间序列实验可用于识别作为生物系统的一部分或响应外部刺激而激活的途径和网络。这些方法通过提供细胞活动的大规模视图，有望彻底改变分子生物学。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和使用各种计算技术组合不同的数据源来实现。该提案的教育部分将通过在研究生和本科生阶段开设新课程，通过在研究和培训方面促进与生物学家的密切互动，以及通过帮助开发和执行卡内基梅隆大学和匹兹堡大学在计算生物学方面的联合博士课程，来开发教授和监督计算机科学、生物学和医学学生的方法。