Robustness and complexity of directed and weighted metabolic hypergraphs

TL;DR

This paper introduces a novel directed hypergraph framework with edge-dependent vertex weights to model metabolic networks, capturing higher-order interactions, directionality, and weights, and uses it to analyze robustness and complexity across various models.

Contribution

It proposes a new hypergraph-based representation for metabolic networks that preserves essential biological information and introduces metrics to quantify their robustness and complexity.

Findings

Antibiotic resistance correlates with high structural robustness.

Complexity distinguishes eukaryotic from prokaryotic organisms.

The framework effectively captures higher-order interactions and directionalities.

Abstract

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 the 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.