A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks

Abstract

Motivation: A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions.Results: Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms.Availability: ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet.Contact: estiyl@bgu.ac.il or michaluz@cs.bgu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

“…Our own work (29,34) and that of other groups (35–37) demonstrated that the addition of functional and tissue-expression data, in combination with basic graph algorithms, enables the construction of PPI networks that are highly relevant to a specific research question. In particular, we showed that when cellular signaling is studied in an infectious or genetic disease context, combining both gene expression information from the tissue affected by the disease and predicted network information flow, serves to highlight important mediators of disease from the PPI hairball surrounding the studied proteins (29).…”

Functional and genomic context in pathway analysis of GWAS data

“…Another group created a framework for assigning context-sensitive weights to protein-protein interaction data [41]. Their method is based on the idea that proteins have preferred interaction partners depending on the cellular context.…”

“…Their method is based on the idea that proteins have preferred interaction partners depending on the cellular context. Applying this framework to identify pathways activated during influenza infection, they found that high-scoring context-sensitive paths in a PPI network were more likely to include biologically relevant proteins (those shown to have a significant effect on viral propagation and interferon production when silenced in an siRNA experiment) [41]. …”

Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis

“…Very few proteins perform their functions alone. Instead, most proteins function by interacting with others to form complexes and/or protein-protein interaction (PPI) networks [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. To understand PPIs at structure level, their 3D structures may be needed.…”