Protein networks in disease

Abstract

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.

A laboratory‐intensive course on the experimental study of protein–protein interactions

“…Proteins rarely act alone; it is through protein-protein interactions that biological processes within cells are achieved. These interactions are critical for the proper functioning within cells, and when disrupted or otherwise altered, often manifest in disease [4,5]. With many organisms (including humans) having sequenced genomes, elucidating the roles of proteins encoded within the genome is a logical next step.…”

Network‐based drugs and biomarkers

“…Recent studies of the relationships between diseases have suggested that drug side-effects can be used to associate diseases [26] or that this can be done through analysis of changes to protein expression [15,27,28]. In addition to this, we have recently shown that multiple diseases converge in a nexus of networks when considering evolutionary conserved regulatory networks [16,17].…”

Gene2DisCo: Gene to disease using disease commonalities

“…They base on gene networks, in which nodes are genes and connections represent precomputed functional relationships among genes, like protein-protein interactions [16], or transcriptional co-expression regulation [17]. Network-based methods differ from each other in the way they exploit disease-genes and their direct connections, ranging from protein-protein interaction network analysis and semi-supervised graph partitioning [17,18], to flow propagation [19], random walks [20], kernelized score functions [21], Gaussian fields and Harmonic functions [22], multiple kernel learning [23], regression trees on mutual information gene networks [24] and network weights adjustment according to a given disease [25].…”