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理解细胞复杂的分子内部机制。在做出决定的机制中，一个基本的组成部分是转录起始的调控网络。本申请的目的是加强模式生物大肠杆菌K-12的转录调控和操纵子组织的主要注释项目。这个项目是先前资助的概念性延续和扩展，支持丰富RegulonDB的注释工作。除了继续文献策展，我们建议进行详尽的基因组中所有可能缺失的启动子位点的映射，以及进行几个理论分析的监管网络，连同模拟和比较在其他实验室进行的微阵列数据。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，使我们能够利用现有的几个数据库将转录，运输，信号转导和代谢放在一起。我们将确定这种细胞知识与几个实验室进行的大量微阵列实验有多少一致性。这些比较需要建立几个计算和化学模型的监管简单和复杂的相互作用，决定基因表达，以及它们的后果，在整个细胞网络（运输，信号转导和代谢）。我们将使用基于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的不同数学标准，以模块，图案和地图的形式分析网络。最后，我们打算对网络的动态进行低分辨率离散模拟，以及对网络的选定映射和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最知名和研究的单细胞生物体的调控网络的经典和活性注释的整体质量和深度。更好地了解整个细胞，将为基础生物学做出重要贡献，从而为了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.