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Advantages and Applications of Gel-Free Proteomic Approaches in the Study of Prokaryotes

Citation

Bowman, JP, Advantages and Applications of Gel-Free Proteomic Approaches in the Study of Prokaryotes, Comprehensive Analytical Chemistry: Applications of Advanced Omic Technologies: From Genes to Metabolites, Elsevier, V Garia-Canas, A Cifuentes, C Simo (ed), Oxford, United Kingdom, pp. 157-200. ISBN 978-0-444-62650-9 (2014) [Research Book Chapter]

Copyright Statement

Copyright 2014 Elsevier

DOI: doi:10.1016/B978-0-444-62650-9.00007-5

Abstract

Proteomics involves the study of the proteins usually in the context of living systems. As proteins are integral to life, their study is crucial for our understanding of the mechanisms underpinning cell biology, physiology, metabolism, and regulation. Integrated with genomics (the study of genomes), transcriptomics (the study of message RNA and gene expression), and metabolomics (the study of cell metabolites), proteomics forms a key component of the science of systems biology. System biologists seek to conceive and measure biological functions at a molecular and mathematical level such that cell processes can be computationally predicted and modeled. As part of this grand concept, proteomics involves measuring protein levels either in absolute terms, deriving a concentration or copy number per cell, or in a relative sense where protein abundance is estimated between one situation and another situation or a control. These approaches allow detailed understanding of functions within a life-form with the data translated to assess metabolic pathway enzyme levels, arrangement and responses of regulatory systems, and responses and interactions of protein complexes that represent the apparatuses making up a living entity. Global proteomics essentially measures as many as possible of these proteins that can be extracted and detected. Global studies provide a broad scope to understand phenotypes as a predictable suite of protein abundance changes. The relatively low number of proteins, lower complexity in terms of compartmentalization, and generally direct regulatory and signaling systems make proteomics very amenable for the study of prokaryote systems biology. This is especially the case for global studies in which a large proportion of a proteome is potentially analyzable with modern techniques, thus providing a comprehensive means to assess microbial systems within a myriad of scenarios.

This chapter focuses on the application of quantitative proteomics in microbial systems and covers recent methodological developments in the field and includes an indication of how it is impacting the study of prokaryotes. In particular, this chapter examines gel-free, label-free-based proteomic analysis and its advantages and disadvantages. The review also covers new developments in mass-spectrometric analysis of proteins and summarizes the means available for downstream data processing to quantify proteins. The chapter also briefly examines the assessment of functionally and regulatory allied groups of proteins via data visualization and also looks at the current trends in proteomic data being integrated into metabolic models. Numerous reviews and continual new method developments mentioned in this chapter indicate that proteomics is a highly active area that is riding the information deluge wave created with next-generation sequencing-driven analysis of genomes and metagenomes.

Item Details

Item Type:Research Book Chapter
Keywords:proteomics, bacteria, food safety, systems biology, Listeria monocytogenes
Research Division:Biological Sciences
Research Group:Biochemistry and Cell Biology
Research Field:Proteomics and Intermolecular Interactions (excl. Medical Proteomics)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Biological Sciences
Author:Bowman, JP (Associate Professor John Bowman)
ID Code:91603
Year Published:2014
Deposited By:TIA - Research Institute
Deposited On:2014-05-22
Last Modified:2015-03-30
Downloads:3 View Download Statistics

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