Gene Regulation E. coli Database Integrated Modeling

描述（由申请人提供）：位于Cuernavaca的墨西哥国立自治大学（UNAM）基因组科学中心和一组合作者提出进一步发展和丰富RegulonDB数据库。RegulonDB包含所有生物体中最大的电子可获得的调节网络，即大肠杆菌K-12。在该项目中，我们将扩展设计，实现计算和概念模型，并从监管网络的不同组成部分的原始文献中生成注释。我们将在不同条件下实验绘制启动子和转录单元。该项目扩展了先前支持RegulonDB的拨款。高质量的人工管理将丰富转录起始之外的内容，包括连接传感与调节的信号转导，以及自然语言文本处理工具。我们建议继续对基因组中所有可能的转录起始位点进行详尽的实验测绘，并对不同条件下的转录单位进行详尽的实验鉴定。RegulonDB的核心（基于文献的网络和图形和浏览工具）将作为可选的用户选择，与高通量可用实验（微阵列和ChIP- ChIP）作为原始和预分析数据，以及所有网络元件（转录因子，启动子，结合位点，操作子，调节相互作用）的计算预测集合相连接并使其可访问。这将基于轨道表示，并符合国际联盟标准化路径交换格式。RegulonDB数据和内部结构的开放性将大大扩展，同时增强其对分析和表示工具的可用性。公共卫生相关性：RegulonDB在概念和计算设计方面，以及在单个生物体的最大和最具特征的调节网络上收集的数据的数量和质量方面都是一个黄金标准。RegulonDB的改进和扩展可能会加速其他细菌模型的研究，包括病原体。RegulonDB已经成为网络组件的各种计算预测工具的基准。此外，大肠杆菌作为一种概念验证生物学模型，在系统生物学中处于前沿地位，可以开发生物信息学、表征和模拟方面的新方法，克服挑战，以模拟和理解整个细胞的功能。

DESCRIPTION (provided by applicant): The Center for Genomic Sciences of the National Autonomous University of Mexico (UNAM) in Cuernavaca and a group of collaborators propose to further develop and enrich the RegulonDB database. RegulonDB contains the largest electronically-available regulatory network of any living organism, that of Escherichia coli K-12. In the project we will expand the design, implement computational and conceptual models, and generate annotations derived from original literature of the different components of the regulatory network. We will experimentally map promoters and transcription units under different conditions. This project expands a previous grant supporting RegulonDB. High quality manual curation is to be enriched beyond transcription initiation to include signal-transduction connecting sensing with regulation, as well as with natural language text processing tools. We propose to continue an exhaustive experimental mapping of all possible transcription initiation sites in the genome and to perform an exhaustive experimental identification of transcription units in different conditions. The core of RegulonDB (literature-based network and the graphic and browsing tools) will be linked and made accessible, as optional user selections, with high-throughput available experiments (microarrays and ChIP- chip) both as raw and pre-analyzed data, as well as with collections of computational predictions of all network elements (transcription factors, promoters, binding sites, operons, regulatory interactions). This will be based on track representations and compliant with international consortia normalized pathway exchange formats. The openness of RegulonDB data and internal structure will expand considerably, enhancing as well its availability to tools for analysis and representation. PUBLIC HEALTH RELEVANCE: RegulonDB is a gold standard both in its conceptual and computational design, as well as in the amount and quality of data gathered on the largest and best characterized regulatory network of a single organism. The improvements and expansion of RegulonDB may accelerate research in other bacterial models, including pathogens. RegulonDB has served as a benchmark for a variety of computational predictive tools for components of the network. Furthermore, E.coli is at the forefront in systems biology as a proof-of-concept biological model to develop new methodologies in bioinformatics, representation and simulation, to overcome challenges in order to model and understand the functioning of a whole cell.