PT - JOURNAL ARTICLE AU - Kevin A. Murgas AU - Emil Saucan AU - Romeil Sandhu TI - Beyond Pairwise Interactions: Higher-Order Dynamics in Protein Interaction Networks AID - 10.1101/2022.05.03.490479 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.05.03.490479 4099 - http://biorxiv.org/content/early/2022/05/04/2022.05.03.490479.short 4100 - http://biorxiv.org/content/early/2022/05/04/2022.05.03.490479.full AB - 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.Competing Interest StatementThe authors have declared no competing interest.