Computational Prediction of Biological Networks in Microbes and Applications to Cyanobacteria

佐治亚大学获得了一笔资金，用于开发一套计算工具，以支持阐明微生物有机体中的代谢和调节网络。利用这些计算工具结合实验研究，研究小组将预测和表征蓝藻中许多重要的代谢网络及其相关的调节网络。这些预测的结果将是所选目标网络的接线图。生物网络的计算解析将主要依赖于通过比较基因组分析和微阵列基因表达数据的解释获得的信息。目前已有超过40种蓝藻基因组测序或正在测序中，为网络解析提供了丰富的信息来源。此外，大量的微阵列数据已经或正在生成各种蓝藻生物，这些数据将在不久的将来公开访问。利用这些信息，许多计算工具将被开发出来，以获得尽可能多的基因功能和关联信息，然后这些信息将被用作阐明生物网络的约束。这些工具将包括(a)基因功能预测工具，(b)操纵子和调控子预测工具；(c)蛋白质-蛋白质和蛋白质- dna相互作用的预测；(d)绘制跨基因组的路径/网络（可能是部分的）；(e)预测在多个微生物有机体中保守的功能模块；(f)代谢和调节网络接线图的预测。已测序的蓝藻基因组的目标网络包括(a)光合作用及其对不同环境因子的适应，(b)氮同化，(c)磷同化，(d)碳固定，(e)铁同化和调节以及(f)渗透调节。预测将基于公开实验数据和本项目生成的数据。每个工具都将为微生物基因组分析和网络解析提供新的有用的计算工具池。一般来说，只要目标生物具有其基因组和一些相关基因组测序，并且具有与目标网络相关的微阵列基因表达数据，则计算工具可用于微生物生物的网络解析。计划预测能力的综合性质和系统方法将为微生物网络阐明提供一个非常有效和可转移的框架。其他研究人员可以直接在他们自己的路径和网络研究中使用这个框架和开发的工具，因为所有的预测程序都将作为开源提供。本项目为本科生和研究生学习解决复杂生物问题的生物信息学工具开发和应用提供了一个理想的训练基地。本项目的研究成果将开发和教授新的生物信息学和基因组学课程。此外，一年一度的培训讲习班将教授感兴趣的微生物学家如何使用本项目开发的工具和其他相关工具。

The University of Georgia is awarded funds to develop a suite of computational tools in support of elucidation of metabolic and regulatory networks in microbial organisms. Using these computational tools in conjunction with experimental investigation, the team will predict and characterize a number of important metabolic networks and their associated regulatory networks in cyanobacteria. The outcome of these predictions will be wiring diagrams of the selected target networks. The computational elucidation of biological networks will rely mainly on information derived through comparative genome analyses and interpretation of microarray gene expression data. Currently over 40 cyanobacterial genomes have been sequenced or are in the process of being sequenced, providing a rich source of information for network elucidation. In addition, substantial microarray data has been or is being generated for various cyanobacterial organisms, which are or will be publicly accessible in the near future. Using such information, a number of computational tools will be developed to derive as much gene function and association information as possible, which will be then used as constraints in the elucidation of biological networks. These tools will include tools for (a) gene function prediction, (b) prediction of operons and regulons; (c) prediction of protein-protein and protein-DNA interactions; (d) mapping pathways/networks (possibly partial) across genomes; (e) prediction of functional modules that are conserved across multiple microbial organisms; and (f) prediction of the wiring diagrams of metabolic and regulatory networks. The target networks in cyanobacteria with sequenced genomes include (a) photosynthesis and its acclimation to different environmental factors, (b) nitrogen assimilation, (c) phosphorus assimilation, (d) carbon fixation, (e) iron assimilation and regulation and (f) osmolarity regulation. The predictions will be based on both public experimental data and data generated in this project. Each of the tools will provide a new and useful addition to the current pool of computational tools for microbial genome analysis and network elucidation. The computational tools can be used for network elucidation for microbial organisms in general as long as a target organism has its genome and some related genomes sequenced and has microarray gene expression data available relevant to the target networks. The comprehensive nature and the systemic approach of the planned prediction capability will provide a highly effective and transferable framework for microbial network elucidation in general. Other researchers can directly use this framework and the tools developed in this project in their own investigation of pathways and networks as all the prediction programs will be provided as open source. This project provides an ideal training ground for both undergraduate and graduate students to learn bioinformatics tool development and application for solving complex biological problems. New bioinformatics and genomics courses will be developed and taught based on the research results of this project. In addition, annual training workshops will teach interested microbiologists the use of the tools developed in this project and other related tools.