Computational Prediction of Biological Networks in Microbes and Applications to Cyanobacteria

点击翻译按钮获取中文摘要

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.