Arabidopsis 2010: Visual Informatics Tools to Interactively Link Arabidopsis Metabolic and Regulatory Network Maps with Genome-Wide Expression Data

新兴的分子技术正在使收集大量数据集成为可能，这些数据集包含关于生物体中完整的mRNA、蛋白质和代谢物的信息。这些数据集详细说明了不同器官、组织和细胞类型中的mRNAs、蛋白质和代谢物水平随时间的变化以及有机体受到环境压力的情况。拟南芥作为模式植物系统的发展，以及随后在拟南芥2010计划中对功能基因组学的重视，导致了大量这样的数据集的产生。这些数据集可以比较野生型和转基因拟南芥，以了解单个基因的变化如何影响整个基因组的功能。总体目标是了解一个简单的植物系统中所有基因之间发生的相互作用，作为开发一个逻辑框架的基础，以改进具有重要农学意义的植物物种的组成和产量。计算技术对于整合和解释这些大型数据集是必不可少的。其目标是开发和实施强大的软件工具，用于分析全基因组表达数据。软件工具将由三个模块组成，这些模块将把mRNA、蛋白质和代谢物数据集的可视化与我们目前对拟南芥代谢和调控网络的理解结合在一起。它们是：1)具有交互式图形显示的代谢和调节网络绘图功能。2)数据集成能力，其中可以将全球概况数据聚集、可视化、叠加在代谢和调节地图上。3)使用模糊认知图对网络中的新陈代谢或调节流进行建模的建模能力。这些工具将被生物界用来产生和测试有关代谢途径调节的假说，这些假说有助于植物的最终组成和产量。这项为期24个月的成果将使用来自拟南芥的测试数据集为该软件提供概念验证。拟议的多学科研究将为将基因组学和生物信息学教育整合到本科和研究生课程中提供宝贵的机会。三-五名本科生和两名研究生将在两年的课程中参与。此外，计划与当地高中教师进行互动，使有动机的高中生能够参与到这项研究中来。还有一项计划，将代表人数不足的群体的学生纳入研究项目。该项目中使用的高功率计算机显卡将进一步增强接触年轻、精通计算机的学生的能力，即使在高中也是如此。这个软件的开发将直接导致它作为中学教育的教学工具。这种协调的方法显示代谢网络、细胞功能和数据集的多个视图，将把抽象数据放在它产生的背景下，并促进代谢网络中的不确定性和变异性的研究。尽管该软件将专门针对拟南芥的生物学，但它可以进行修改，以用于任何物种。这些工具和数据库将免费提供给科学界用于学术目的http://www.botany.iastate.edu/~mash/metnetex/metabolicnetex.html.它们将提供一个新的框架，以制定关于特定基因功能的可检验假说，并扩大我们对新陈代谢的整体理解。

Emerging molecular technologies are enabling the collection of massive data sets that contain information about the full complement of mRNAs, proteins and metabolites in an organism. These data sets detail the fluctuations in levels of mRNAs, proteins, and metabolites in different organs, tissues and cell types, over time, and as the organism is subjected to environmental stresses. The development of Arabidopsis as a model plant system, and the ensuing emphasis on functional genomics in the Arabidopsis 2010 program, is leading to the generation of a multitude of such data sets. These data sets can compare wild-type and genetically modified Arabidopsis to understand how a change in a single gene can affect the function of the entire genome. The overall goal is to understand the interactions occurring between all the genes in a simple plant system, as the basis for developing a logical framework to improve the composition and yield of agronomically important plant species.Computational technologies are essential to integrate and interpret these large data sets. The goal is to develop and implement powerful software tools for analysis of genome-wide expression data. The software tools will consist of three modules that will integrate the visualization of the mRNA, protein, and metabolite data sets with our current understanding of the Arabidopsis metabolic and regulatory network. These are: 1) A metabolic and regulatory network mapping capability with an interactive graph display. 2) A data integration capability, wherein global profiling data can be clustered, visualized, superimposed on the metabolic and regulatory map. 3) A modeling capability in which the metabolic or regulatory flow in the network is modeled using fuzzy cognitive maps. These tools will be used by the biological community to generate and test hypotheses about the regulation of metabolic pathways that contribute to the final composition and yield of the plant. The 24-month deliverable will provide a proof-of-concept for the software, using test data sets from Arabidopsis.The proposed multidisciplinary research will provide invaluable opportunities for the integration of genomics and bioinformatics education into undergraduate and graduate curricula. Three-five undergraduate students and two graduate students will be involved over the course of two years. In addition, interations with local high school teachers are planned to enable motivated high school students to participate in this research. There is also a plan to integrate students from underrepresented groups into the research project. The ability to reach young, computer savvy students even in high school will be further enhanced by the high power computer graphics used in the project. Development of this software will lead directly to its use as a teaching tool in secondary education.This coordinated approach to displaying multiple views of metabolic networks, cellular functions, and datasets will put abstract data in the context in which it arises, and facilitate studies of uncertainty and variation in metabolic networks. Although the software will be directed specifically towards the biology of Arabidopsis, it can be modified for use in any species. These tools, and the database will be freely available to the scientific community for academic purposes http://www.botany.iastate.edu/~mash/metnetex/metabolicnetex.html. They will provide a novel framework to formulate testable hypotheses regarding the function of specific genes and expand our holistic understanding of metabolism.