Abstract
Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.
Highlights
Biology has entered a new era of scientific discoveries as a consequence of the development of new technologies, and the production of massive amounts of biological data at the cellular and subcellular levels
Among the multiple computational analyses that can be performed for the reconstruction of PPIN and prediction of interactions (Table 4), we will focus on phylogenetic methods, used in bacterial pathogens (Albert, 2007)
We have reviewed the metabolic, protein-protein and regulatory networks that have helped understanding disease, mechanisms of pathogenesis and virulence, as well as interactions between bacteria and their hosts
Summary
Biology has entered a new era of scientific discoveries as a consequence of the development of new technologies, and the production of massive amounts of biological data at the cellular and subcellular levels. The biological networks have a specific topology that tends to be small-world; in other words, their organization makes possible the existence of hubs as central focal points of interactions (Aloy and Russell, 2004), e.g., proteins or genes involved in regulatory processes of bacterial pathogenicity or plant resistance (Haynes et al, 2006). Another powerful structural measure is the clustering coefficient, which evaluates the degree of grouping between a node and its neighbors. Summary of structural measurements of the topology of a network and their utility in a biological context
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