Computational Prediction of Biological Networks in Microbes and Applications to Cyanobacteria

微生物生物网络的计算预测及其在蓝藻中的应用

• 批准号: 0542119
• 负责人: Ying Xu
• 金额: $ 187.58万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0542119&HistoricalAwards=false
• 关键词: Computational; Prediction; Biological; Networks; Microbes

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.

格鲁吉亚大学获得资金开发一套计算工具，以支持微生物生物体中代谢和调控网络的阐明。使用这些计算工具与实验研究相结合，该团队将预测和表征蓝藻中一些重要的代谢网络及其相关的调控网络。这些预测的结果将是所选目标网络的布线图。生物网络的计算解释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种蓝藻基因组已被测序或正在测序，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产生的各种蓝藻生物，这是或将在不久的将来公开访问。利用这些信息，将开发一些计算工具，以获得尽可能多的基因功能和关联信息，然后将其用作阐明生物网络的约束条件。这些工具将包括用于以下方面的工具：（a）基因功能预测;（B）操纵子和调节子预测;（c）蛋白质-蛋白质和蛋白质-DNA相互作用预测;（d）绘制跨基因组的通路/网络（可能是部分的）;（e）预测在多种微生物有机体中保守的功能模块;（f）预测代谢和调节网络的线路图。蓝藻的目标网络包括（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，（f）渗透压调节。这些预测将基于公开的实验数据和该项目产生的数据。每一个工具将提供一个新的和有用的除了目前池的计算工具，用于微生物基因组分析和网络阐明。计算工具一般可用于微生物生物体的网络阐明，只要目标生物体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可用微阵列基因表达数据。计划的预测能力的综合性和系统性方法将为微生物网络的阐明提供一个高度有效和可转移的框架。其他研究人员可以直接使用这个框架和在这个项目中开发的工具，在他们自己的路径和网络的调查，因为所有的预测程序将作为开源提供。该项目为本科生和研究生提供了一个理想的训练基地，学习生物信息学工具的开发和应用，以解决复杂的生物学问题。新的生物信息学和基因组学课程将根据该项目的研究成果开发和教授。此外，每年的培训讲习班将向感兴趣的微生物学家教授本项目开发的工具和其他相关工具的使用。