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Updated: Apr 29, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Network motifs that recur across species, including gene regulatory and protein-protein interaction networks
Robert Borotkanics1, Harold Lehmann
1Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA, rborotk1@jhu.edu.
Abstract:
Cellular molecules interact in complex ways, giving rise to a cell's functional outcomes. Conscientious efforts have been made in recent years to better characterize these patterns of interactions. It has been learned that many of these interactions can be represented abstractly as a network and within a network there in many instances are network motifs. Network motifs are subgraphs that are statistically overrepresented within networks. To date, specific network motifs have been experimentally identified across various species and also within specific, intracellular networks; however, motifs that recur across species and major network types have not been systematically characterized. We reason that recurring network motifs could potentially have important implications and applications for toxicology and, in particular, toxicity testing. Therefore, the goal of this study was to determine the set of intracellular, network motifs found to recur across species of both gene regulatory and protein-protein interaction networks. We report the recurrence of 13 intracellular, network motifs across species. Ten recurring motifs were found across both protein-protein interaction networks and gene regulatory networks. The significant pair motif was found to recur only in gene regulatory networks. The diamond and one-way cycle reversible step motifs were found to recur only in protein-protein interaction networks. This study is the first formal review of recurring, intracellular network motifs across species. Within toxicology, combining our understanding of recurring motifs with mechanism and mode of action knowledge could result in more robust and efficient toxicity testing models. We are sure that our results will support research in applying network motifs to toxicity testing.
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