Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs

Area of Science:

• Systems Biology
• Bioinformatics
• Network Science

Background:

• Metabolic networks are crucial for understanding organismal function and resilience.
• Existing models often struggle to capture higher-order interactions and reaction directionality.

Purpose of the Study:

• To develop a novel framework for representing metabolic networks using directed hypergraphs.
• To introduce metrics for quantifying network robustness and complexity.
• To analyze the relationship between network structure and biological properties.

Main Methods:

• Representing metabolic networks as directed hypergraphs with edge-dependent vertex weights.
• Defining and applying communicability and search information metrics.
• Analyzing a small-scale *E. coli* core model.
• Comparing 30 diverse metabolic models.

Main Results:

• The directed hypergraph framework effectively preserves essential metabolic network information, including higher-order interactions and directionality.
• Network directionality impacts robustness and complexity measures.
• High structural robustness in metabolic networks is linked to antibiotic resistance.
• Network complexity can differentiate between eukaryotic and prokaryotic organisms.

Conclusions:

• Directed hypergraphs offer a powerful new representation for metabolic networks.
• Network structure, particularly robustness and complexity, provides insights into organismal traits like antibiotic resistance and evolutionary classification.