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Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs.

Pietro Traversa1,2,3, Guilherme Ferraz de Arruda3, Alexei Vazquez4

  • 1Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain.

Entropy (Basel, Switzerland)
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We introduce a novel directed hypergraph framework to model metabolic networks, capturing complex interactions. This approach reveals that high structural robustness correlates with antibiotic resistance, and complexity differentiates organism types.

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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.