Gene Regulation E.coli Database Integrated Modeling

描述（由申请人提供）：后基因组生物学的一个主要挑战是对细胞复杂分子内部机制的理解。决策机制的一个基本组成部分是转录起始的调控网络。本应用程序的目的是加强对模式生物大肠杆菌K-12的转录调控和操纵子组织的主要注释项目。该项目在概念上延续和扩展了先前支持丰富RegulonDB的注释工作的拨款。除了继续进行文献整理之外，我们建议对基因组中所有可能缺失的启动子位点进行详尽的测绘，并对调控网络进行一些理论分析，同时与其他实验室的微阵列数据进行模拟和比较。我们计划用计算预测来补充文献知识，并积极寻找缺失的环节，这将使我们能够利用几个现有的数据库将转录，转运，信号转导和代谢结合在一起。我们将确定这种细胞知识在多大程度上与几个实验室进行的大量微阵列实验相一致。这些比较需要建立几个计算和化学模型，以确定基因表达的简单和复杂的调节相互作用，以及它们在整个细胞网络（运输、信号转导和代谢）中的影响。我们将根据网络连通性的统计特性，以及条件和微阵列实验的生物学知识，使用不同的数学标准，分析模块、基序和地图中的网络。最后，我们打算对网络的动态进行低分辨率离散模拟，以及对选定的地图和网络子组件进行详细的随机建模。建模及其与实验的比较将提高对最知名和研究的单细胞生物调控网络的经典和主动注释的整体质量和深度。更好地了解整个细胞，将为基础生物学提供重要贡献，从而为理解健康和疾病奠定基础。

DESCRIPTION (provided by applicant): A major challenge in post-genomic biology is the understanding of the complex molecular internal machinery of the cell. A fundamental component in the machinery to take decisions is the regulatory network of transcription initiation. The purpose of this application is to enhance the major annotation project on transcriptional regulation and operon organization of the model organism Escherichia coil K-12. This project is a conceptual continuation and expansion of a previous grant supporting the annotation work enriching RegulonDB. In addition to continuing literature curation, we propose to perform an exhaustive mapping of all possible missing promoter sites in the genome, as well as to perform several theoretical analyses of the regulatory network, together with simulations and comparisons with microarray data performed in other laboratories. We plan to complement literature knowledge with computational predictions, and to actively search for the missing links that will permit us to put together transcription, transport, signal transduction and metabolism, taking advantage of several existing databases. We will determine how much this cellular knowledge is consistent with the large amounts of microarray experiments performed in several laboratories. These comparisons require building several computational and chemical models of regulatory simple and complex interactions determining gene expression, as well as their consequences in the overall cellular network (transport, signal transduction and metabolism). We will analyze the network in modules, motifs, and maps, using different mathematical criteria based on the statistical properties of the network connectivity, as well as on biological knowledge of conditions and microarray experiments. Finally, we intend to perform low-resolution discrete simulations of the dynamics of the network, as well as detailed stochastic modeling of selected maps and subcomponents of the network. Modeling and its comparison with experiments shall enhance the overall quality and depth of the classical and active annotation of the regulatory network of the best known and studied single cell organism. Having a better understanding of a whole cell, shall provide an important contribution to basic biology and thus to the foundations to understand health and disease.