Identifying Responsive Functional Modules from Protein-Protein Interaction Network

Abstract

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

Integrative approaches for finding modular structure in biological networks

“…As these profiles capture dynamic and process-specific information correlated with cellular or disease states, they naturally complement interaction data, which are primarily derived under a single experimental condition. Computational integration of network and ‘omics’ profiles has thus become a popular strategy for extracting context-dependent active modules, which mark regions of the network showing striking changes in molecular activity (e.g., transcriptomic expression) or phenotypic signatures (e.g., mutational abundance) associated with a given cellular response 4,30-38 (Figure 1; these regions have alternatively been described as network hotspots 39,40 or responsive subnetworks 41-43 ).…”

“…The first class of methods, themed SigArSearch (Significant-Area-Search) 31,33,48 was previously reviewed 43 . Many of these methods 33,41,44,48-56 descend from an early formulation, JActiveModules 48 , (also implemented as an application tool through the network analysis/visualization platform, Cytoscape 57 ; Table 1), which was the first to frame the active modules search task as an optimization problem.…”

Functional Context Network of T2DM

“…However, heuristic methods cannot guarantee optimal solutions and are highly sensitive to parameter settings. A review over the progress in computational methods for finding functional modules is given by Wu et al (2009). A major progress was introduced with an algorithm (Dittrich et al 2008) that computes exact solutions for the MWCS problem in reasonable time.…”

Sensitive detection of pathway perturbations in cancers

“…More recent methods have sought to capture information about the activation of a pathway from the perspective of the interactions in it. A number of these techniques, reviewed by [9], have been developed for case-control data, for which we can compute p -values reflecting the statistical significance of the differential expression of each gene between the samples in the treatment and those in the control [10-14]. Draghici et al [10] combined a term that captured the significance of the genes in a pathway with an additional weighted term that measured how well the data matches the expected pattern of induction and repression, as encoded by the interactions in the pathway.…”