Development and Support of the Pathway Tools Software [SRI Proposal ECU 11-614]

描述（由申请人提供）：生物体（如致病细菌）基因组序列的完成标志着加速研究阶段的开始，现在可以导致生物体定量代谢通量模型的产生。这种代谢通量模型有多种应用。它们使对生化途径的基本科学理解成为可能。它们可以通过计算预测代谢网络中哪些基因和反应是必需的，从而促进了针对致病细菌的药物设计。它们支持新的代谢途径的工程设计，例如用于生物能源研究。基因组序列所定义的大量数据和知识，以及生物体过去和未来的大量实验发现，需要使用数据库作为有关基因组、生化网络和生物体调节过程的信息的中央存储库。Pathway Tools软件是一个强大而全面的系统，用于构建生物特异性数据库和基因组通路分析。Pathway Tools使科学家社区能够创建、查询、可视化、分析MODs，并在web上发布MODs。Pathway Tools支持构建结合大量生物信息学数据类型的MODs，包括基因组图谱、基因、操纵子、rna、蛋白质、化合物、生化反应、代谢途径和调控相互作用。Pathway Tools是一个成熟的生产级软件环境，已经被SRI之外的264个小组（见附录）用于分析1,969个基因组。我们建议对新的Pathway Tools模块进行重大改进，以生成个体生物和微生物群落的代谢通量模型。该工具允许比同类工具更快地创建这些复杂模型。我们建议对路径工具的web界面进行重大的重新设计。我们建议增强Pathway Tools网络代谢网络图和调节网络图，它们提供交互式的、基于web的蜂窝网络图。我们建议创建一个变更报告系统，通知科学家对Pathway Tools数据库的更新。我们建议创建一个工具来推断测序基因组中的多聚体蛋白复合物，并通过几个序列相关操作扩展Pathway Tools。我们建议为Pathway Tools的庞大且不断增长的用户社区提供支持服务，维护软件的质量文档，并每年创建两个经过彻底测试的软件版本。

DESCRIPTION (provided by applicant): The completion of the genome sequence for an organism such as a disease-causing bacterium marks the start of a phase of accelerated research that can now lead to the production of a quantitative metabolic flux model for the organism. Such metabolic flux models have diverse applications. They enable basic scientific understanding of biochemical pathways. They allow computational predictions of which genes and reactions within the metabolic network are essential, thus facilitating the design of drugs against disease-causing bacteria. They support the engineering of new metabolic pathways, such as for bioenergy research. The huge quantity of data and knowledge defined by the genome sequence, and by the large number of past and future experimental findings for the organism, requires using a database as a central repository for information about the genome, the biochemical network, and the regulatory processes of that organism. The Pathway Tools software is a robust and comprehensive system for constructing organism-specific databases, and for pathway analysis of genomes. Pathway Tools enables communities of scientists to create, query, visualize, analyze MODs, and to publish the MODs on the web. Pathway Tools supports construction of MODs that combine a large number of bioinformatics datatypes, including genome maps, genes, operons, RNAs, proteins, chemical compounds, biochemical reactions, metabolic pathways, and regulatory interactions. Pathway Tools is a mature and production grade software environment that has been used by 264 groups outside SRI (see Appendix) to analyze 1,969 genomes. We propose major improvements to the new Pathway Tools module for producing metabolic flux models for individual organisms and for microbial communities. This tool allows much faster creation of these complex models than does comparable tools. We propose to perform a major redesign of the Pathway Tools web interfaces. We propose to enhance the Pathway Tools web metabolic network diagram and regulatory network diagram, which provide interactive, web-based cellular network maps. We propose to create a change-reporting system for notifying scientists of updates to Pathway Tools databases. We propose to create a tool for inferring multimeric protein complexes in sequenced genomes, and to extend Pathway Tools with several sequence-related operations. We propose to provide support services for the large and growing user community for Pathway Tools, to maintain quality documentation for the software, and to create two thoroughly tested releases of the software per year. PUBLIC HEALTH RELEVANCE: The proposed project will generate software tools that increase the ability of scientists to use genome sequence data to advance human health. This software enables creating central database repositories and websites con- training genome data and data on cellular networks for disease-causing bacteria, and for experimental model organisms such as the laboratory mouse. The software allows researchers to access the most up-to-date information about those organisms using intuitive querying tools, and provides scientific visualization capabilities that aid scientists in more quickly understanding large, complex collections of data, and applying that data to advance health.