BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.