Conceptual Data Integration for the VirtualPlant

纽约大学被授予开发新的信息学、可视化、数学和统计工具的奖项，以实现细胞中分子网络的动态建模和可视化。这些工具将被集成到一个公共平台中，以便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、途径和网络。这一努力的成功将使研究人员(1)能够从机械上解释分子网络变化是如何引起反应的，以及(2)在未经测试的条件下或修改后预测分子网络状态。预测模型对于开发干预实际应用的分子网络的策略很有用。该项目有四个主要目标：(1)集成：将基因、蛋白质和分子之间的已知关系(从公共数据库、出版的文献中提取并使用预测算法生成)与实验测量(例如，信使核糖核酸测量)相结合。(2)可视化：开发新的可视化技术，将多变量信息呈现为便于提取生物学概念的可视格式。(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：开发在未经测试的条件下预测分子网络状态的算法。这些方法将被结合在一个名为“虚拟植物”的系统中，该系统可能成为基因组数据的系统生物学分析的基本工具箱，并为未来高通量数据的分析提供一个框架。

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.