Network Approaches to the Functional Analysis of Microbial Proteins

• Published in: Advances in Microbial Physiology Vol. 59; pp. 101 - 133
• Main Authors: Hallinan, J.S., James, K., Wipat, A.
• Format: Book Chapter Journal Article
• Language: English
• Published: United Kingdom Elsevier Science & Technology 2011
• Subjects: Animal physiology; Bacteria - chemistry; Bacteria - genetics; Bacteria - metabolism; Bacterial Proteins - chemistry; Bacterial Proteins - genetics; Bacterial Proteins - metabolism; Databases, Genetic; Integrative bioinformatics; Microbial systems biology; Microbiology (non-medical); Network analysis; Protein Interaction Mapping - methods; Network analysis; Integrative bioinformatics; Microbial systems biology
• ISBN: 0123876613; 9780123876614
• ISSN: 0065-2911; 2162-5468
• DOI: 10.1016/B978-0-12-387661-4.00005-7

Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein–protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.