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bioRxiv · 10.1101/2022.05.03.490479

Beyond Pairwise Interactions: Higher-Order Dynamics in Protein Interaction Networks

Murgas, K. A.·Saucan, E.·Sandhu, R.
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Abstract

Protein interactions form a complex dynamic system that shapes cell phenotype and function; in this regard, network analysis is a powerful tool for studying the dynamics of cellular processes. Graph-based models are limited, however, in that these models consider only pairwise relationships. Higher-order interactions are well-characterized in biology, including protein complex formation and feedback or feedforward loops. These higher-order relationships are better represented by a hypergraph as a generalized network model. Here, we present an approach to analyzing dynamic gene expression data using a hypergraph model and quantify network heterogeneity via Forman-Ricci curvature. We observe, on a global level, increased network curvature in pluripotent stem cells and cancer cells. Further, we use local curvature to conduct pathway analysis in a melanoma dataset, finding increased curvature in several oncogenic pathways and decreased curvature in tumor suppressor pathways. We compare this approach to a graph-based model and a differential gene expression approach.

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Murgas, K. A., Saucan, E., Sandhu, R.. 2022-05-04. Beyond Pairwise Interactions: Higher-Order Dynamics in Protein Interaction Networks. https://doi.org/10.1101/2022.05.03.490479

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