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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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EHMN 2026: A Thermodynamically Refined, SBML-Standardised Human Metabolic Network for Genome-Scale Analysis and QSP

Igor Goryanin^1,2, Leonid Slovianov², Stephen Checkley²

¹School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK.

|April 27, 2026

Summary

The Edinburgh Human Metabolic Network (EHMN) 2026 is a refined human metabolic model. It enhances data standardization and thermodynamic accuracy for improved systems biology research and drug development applications.

Keywords:

SBML standardisation flux balance analysis (FBA)genome-scale metabolic model (GEM)human metabolic reconstruction quantitative systems pharmacology (QSP)systems biology interoperability thermodynamic refinement

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Last Updated: Apr 28, 2026

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Area of Science:

Systems Biology
Metabolic Engineering
Computational Biology

Background:

Genome-scale metabolic models (GEMs) are crucial for understanding human metabolism in health and disease.
Legacy models often suffer from inconsistent identifiers, incomplete pathways, and lack of thermodynamic rigor, hindering research.
These limitations impede reproducibility, interoperability, and the translation of findings into clinical applications.

Purpose of the Study:

To present EHMN 2026, an updated and refined human metabolic reconstruction.
To improve data standardization, pathway integration, and thermodynamic accuracy of the model.
To enhance the model's utility for systems biology, quantitative systems pharmacology, and multi-layer integration.

Main Methods:

Systematic reconciliation of metabolite and reaction identifiers using MetaNetX and ChEBI.
Consolidation of duplicate reactions and assessment of thermodynamic directionality.
Structured pathway annotation using Reactome and encoding in SBML Level 3 Version 2 with FBC2 package.

Main Results:

EHMN 2026 includes 11 compartments, 14,321 metabolites, and 22,642 reactions with 42.6% gene-protein-reaction associations.
Pathway integration mapped 2194 Reactome identifiers, and thermodynamic refinement eliminated infeasible cycles.
The model is SBML-compliant, portable, and features enhanced annotation and an 11-compartment architecture for organelle-specific modeling.

Conclusions:

EHMN 2026 provides a harmonized, thermodynamically refined, and pathway-annotated human metabolic model.
The model offers improved annotation depth and standards-based interoperability for reproducible metabolic analysis.
It serves as a robust foundation for future systems pharmacology and integrative modeling studies.