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.

卡内基 - 梅隆大学被授予了牢房中的模型动态系统的赠款。基因表达是一个时间过程，细胞中的大多数系统都受到连续调节。新的高通量生物学技术可以生成大量的静态和时间序列数据。时间序列实验可用于识别被激活的途径和网络，这些途径和网络被激活为生物系统的一部分或响应外部刺激。这些方法有望通过提供细胞活性的大规模观察来革命分子生物学。但是，时间序列数据集还引入了许多新的计算挑战。在此项目中，专门为此类实验设计的算法将被开发出来，以利用此类数据中的独特功能并解决它们引起的独特问题。这将通过开发用于分析基因表达数据的计算方法，并使用各种计算技术组合不同的数据源。该提案的教育部分将通过在研究生和本科生中开发新的班级，在研究和培训中与生物学家进行密切互动，并帮助开发和执行Carnegie Mellon University和匹兹堡大学的计算生物学联合博士学位课程，从而开发方式来教授和监督计算机科学，生物学和医学生教学，生物学和医学生。