Hypergraph geometry reflects higher-order dynamics in protein interaction networks.

ABSTRACT: Protein interactions form a complex dynamic molecular system that shapes cell phenotype and function; in this regard, network analysis is a powerful tool for studying the dynamics of cellular processes. Current models of protein interaction networks are limited in that the standard graph model can only represent 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 pluri

SUBMITTER: Murgas KA 

PROVIDER: S-EPMC9719542 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature