Robustness and complexity of directed and weighted metabolic hypergraphs

Traversa, Pietro ; Ferraz de Arruda, Guilherme ; Vazquez, Alexei ; Moreno, Yamir (Universidad de Zaragoza)

Resumen: Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

Idioma: Inglés

DOI: 10.3390/e25111537

Año: 2023

Publicado en: ENTROPY 25, 11 (2023), 1537 [16 pp.]
ISSN: 1099-4300

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/E36-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/FONDO-C19-UZ-164255
Financiación: info:eu-repo/grantAgreement/EC/H2020/101017453/EU/Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity/KATY

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Teórica (Dpto. Física Teórica)