Information Flow Analysis of Interactome Networks

Abstract

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

“…'interactomics', thus represents one of the important directions of functional proteomics and it could provide novel insight into microbial cellular metabolism (Singh & Nagaraj, 2006). In addition, the interaction between proteins and DNA is crucial for regulating gene expression and thus key to cellular regulatory networks (Missiuro et al, 2009). The interactome of cells can generally be obtained by three approaches.…”

Constrained Network Modularity

“…Thus, network integration of gene expression values is expected to improve module detection power by computational algorithms and further corroborate the protein modularity maps [17]. Evidence for interacting protein pairs in a complex that show mRNA coexpression was for instance provided by Dezso et al, and is also available from human interactome experimental work [18] and tissue-specific interactome analysis [19].…”

Network analysis: a new approach to study endocrine disorders

“…1). Several models have been used for network analysis, from 'Power Graphs' and the 'Information Flow Model', to 'Boolean' and 'Bayesian' networks (Missiuro et al 2009, Pujol et al 2010, Youngs et al 2013. In Bayesian networks, the nodes represent biological variables and directed edges represent causality through conditional probabilities between them.…”