mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Bioinformatics

Background:

• Genome-scale metabolic models are crucial for biological research but face challenges in integration due to incompatible identifier systems.
• Existing methods lack automated tools for merging diverse models from multiple reconstruction pipelines into a unified, comprehensive model.

Purpose of the Study:

• To present mergem, a novel method for comparing, merging, and translating multiple genome-scale metabolic models.
• To overcome identifier incompatibility issues and facilitate the creation of comprehensive metabolic reconstructions.

Main Methods:

• Development of mergem, a method utilizing a universal metabolic identifier mapping system.
• Implementation of mergem as a command-line tool, Python package, and web application (Fluxer).
• Fluxer enables simulation and visual comparison of models using interactive flux graphs.

Main Results:

• Mergem robustly compares models from different pipelines, merges common elements, and translates identifiers.
• The tool successfully integrates diverse metabolic models, overcoming namespace incompatibilities.
• Fluxer provides interactive visualization for comparing metabolic models.

Conclusions:

• Mergem facilitates the curation of comprehensive metabolic reconstructions by merging diverse model drafts.
• The method aids in discovering unique and common metabolic features across different organisms.
• Mergem enhances the utility of genome-scale metabolic models for systems biology research.