Computational Prediction of Biological Networks in Microbes and Applications to Cyanobacteria

佐治亚大学获得了开发一套计算工具的资金，以支持阐明微生物体内的代谢和调节网络。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的一些重要代谢网络及其相关的调控网络。这些预测的结果将是选定目标网络的接线图。生物网络的计算阐明将主要依赖于通过比较基因组分析和对微阵列基因表达数据的解释而获得的信息。目前已有40多个蓝藻基因组测序或正在测序中，为网络解译提供了丰富的信息来源。此外，已经或正在为各种蓝藻生物生成大量的微阵列数据，这些数据在不久的将来可以或将公开获取。利用这些信息，一些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，这些信息将被用作阐明生物网络的约束条件。这些工具将包括以下工具：(A)基因功能预测；(B)操纵子和调节子的预测；(C)蛋白质-蛋白质和蛋白质-DNA相互作用的预测；(D)跨基因组的路径/网络图(可能是部分的)；(E)在多个微生物中保守的功能模块的预测；以及(F)代谢和调控网络的接线图的预测。蓝藻基因组测序的目标网络包括：(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)铁的同化和调节，(F)渗透压调节。这些预测将基于公开的实验数据和本项目产生的数据。每一种工具都将为目前的微生物基因组分析和网络阐明计算工具池提供一个新的和有用的补充。只要目标生物的基因组和一些相关基因组已经测序，并且有与目标网络相关的微阵列基因表达数据，这些计算工具就可以用于一般微生物的网络阐明。计划中的预测能力的全面性和系统性将为微生物网络的总体阐明提供一个高效和可转移的框架。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来研究他们自己的路径和网络，因为所有的预测程序都将以开放源代码的形式提供。该项目为本科生和研究生提供了一个学习生物信息学工具开发和应用以解决复杂生物学问题的理想培训场所。将根据该项目的研究成果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向感兴趣的微生物学家传授该项目开发的工具和其他相关工具的使用。

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.