Conceptual Data Integration for the VirtualPlant

VirtualPlant 的概念数据集成

• 批准号: 0445666
• 负责人: Gloria Coruzzi
• 金额: $ 129.3万
• 依托单位: New York 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: 2005-06-01 至 2011-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0445666&HistoricalAwards=false
• 关键词: Conceptual; Data; Integration; VirtualPlant

New York University is granted an award to develop new informatics, visualization, math and statistic tools to enable dynamic modeling and visualization of molecular networks in cells. These tools will be integrated in a common platform so as to understand how internal and external perturbations affect processes, pathways & networks controlling plant growth and development. Succeeding in this endeavor will allow researchers (1) to explain mechanistically how molecular network changes occur to evoke responses and (2) to predict molecular network states under untested conditions or after modifications. Predictive models are useful for developing strategies that intervene in molecular networks for practical applications. The project has four major goals: (1) Integration: to integrate known relationships among genes, proteins and molecules (extracted from public databases, the published literature and generated with predictive algorithms) with experimental measurements (e.g. mRNA measurements). (2) Visualization: to develop novel visualization techniques that will render the multivariate information in visual formats that facilitate extraction of biological concepts. (3) Synthesis: to integrate mathematical and statistical methods with the novel visualization techniques to help summarize the data and test hypothesis. (4) Predictions: to develop algorithms to make predictions of the molecular network state under untested conditions. These approaches will be combined in a system called "VirtualPlant" which may become an essential toolbox for Systems Biological analysis of genomic data and provide a framework for the analysis of future high-throughput data.

纽约大学被授予一个奖项，以开发新的信息学，可视化，数学和统计工具，使动态建模和可视化的分子网络在细胞中。这些工具将被集成到一个共同的平台，以了解内部和外部扰动如何影响控制植物生长和发育的过程，途径网络。这一奋进的成功将使研究人员（1）机械地解释分子网络变化如何引起反应，（2）预测未经测试的条件下或修改后的分子网络状态。预测模型对于开发干预分子网络以用于实际应用的策略是有用的。该项目有四个主要目标：（1）整合：将基因、蛋白质和分子之间的已知关系（从公共数据库、已发表文献中提取，并使用预测算法生成）与实验测量（例如mRNA测量）相整合。(2)可视化：开发新的可视化技术，以可视化格式呈现多变量信息，便于提取生物学概念。(3)综合：将数学和统计方法与新的可视化技术相结合，以帮助总结数据和检验假设。(4)预测：开发算法，在未经测试的条件下预测分子网络状态。这些方法将结合在一个名为“VirtualPlant”的系统中，该系统可能成为系统生物学分析基因组数据的重要工具箱，并为未来高通量数据的分析提供框架。