A Post-Genomics Approach to Understanding E. coli Network Architecture

最近的计算研究表明，大肠杆菌的代谢和调控网络是一个小世界，其特征通常是存在高度连接的“枢纽”节点，这些节点连接高度内部连接的子网络，因此在塑造整体网络行为方面发挥着重要作用。该项目描述了一种实验和计算相结合的方法，以发现其过度表达对细胞行为有最强影响的基因，并确定这些基因是否显示出具有“小世界”网络结构特征的生物网络连接值。建议应用并进一步发展一种新的、创新的后基因组学方法，即并行基因-性状作图，从而能够在全基因组水平上对大肠杆菌生物网络结构进行新的基于实验的研究。同时，建议进行计算研究，以创建和解析大肠杆菌调节和代谢网络图，以确定单个基因连接性的值。这项工作的一个主要成果将是在生物科学和计算科学的交界处培训几名年轻的科学家和学生。第二个结果将是进一步开发和应用一种可在各种情况下使用的新的后基因组学方法。

Recent computational studies have suggested that E. coli metabolic and regulatory networks are small-world, which are often characterized by the presence of highly connected "hub" nodes that connect highly intra-connected sub-networks and, therefore, play an important role in shaping overall network behavior. This project describes a combined experimental and computational approach to discover genes whose over-expression has the strongest affect on cell behavior and to determine if these genes display biological network connectivity values that are characteristic of "small-world" network architectures. It is proposed to apply and further develop a new and innovative post-genomics methodology, parallel gene-trait mapping that enables new experiment-based investigations of the structure of E. coli biological networks at the whole-genome level. In parallel, it is proposed to perform computational studies that will create and parse E. coli regulatory and metabolic network-graphs to determine individual gene connectivity values. A primary outcome of this work will be the training of several young scientists and students at the interface of the biological and computational sciences. A second outcome will be the further development and application of a novel post-genomics methodology that can be used in a variety of contexts.