Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks

The introduction of high-throughput techniques that generate large data sets has urged the development of systematic approaches to interpret these data and transform them into comprehensible models. The formulation of network models from global protein studies is essential to understand the functioning of organisms. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow distinct insights into functional organization. One such concept is the Protein Interaction Network (PIN), which contains proteins as nodes and undirected edges representing binding events observed in large-scale protein-protein interaction studies. A second concept is the Protein Signaling Network (PSN), in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. The plant field has recently employed high-throughput methods such as mass spectrometry and protein chips to study protein-protein interactions and signaling pathways. Data from nonplant model systems have been used to predict the first global protein interaction networks in plants. These data can be integrated with data collected by other large-scale approaches, such as metabolomics and transcriptomics. Such integrative approaches should aid in understanding the flexibility of plant responses triggered by a frequently changing environment.