Curating MitoCore: A Standardized Small-Scale Human Metabolic Model as Platform for Proteomics Integration and Disease Modeling

Lange, E., Santamaria, A. B. R., Heyer, R.

10.64898/2026.06.29.734258 · was preprinted

biomedical code

Surfaced because: open code.

relevance 0.37 openness 0.25 novelty 0.31

Abstract

MotivationCentral human metabolism powers cellular processes, yet its dysregulation in disease remains poorly understood. While comprehensive genome-scale metabolic models like Human-GEM are available, their size limits interpretability and computational efficiency. Conversely, the smaller MitoCore model is more manageable but lacks the standardized annotations and curated gene-protein-reaction (GPR) associations necessary for omics integration like protein-constrained modeling. Improving MitoCores annotation quality is therefore essential for its use in integrative workflows. ResultsWe systematically updated MitoCore to enhance compatibility with the protein-constrained modeling framework sMO-MENT. By restructuring legacy annotations and integrating data from Human-GEM and MitoMammal, we increased EC-codes from 354 to 593 and UniProt-annotated genes from 0 to 592. MitoCore captures central metabolic processes, confirmed by mapping its reactions to 51 of 106 metabolic KEGG modules. Integration of thrombocyte proteomics and experimental ATP data for original and curated models showed an increase in mapped proteins (228 to 294) and reactions with kcat values (295 to 310), adding 43 protein-constrained reactions. Consequently, prediction errors for exchange fluxes and ATP production decreased by 19% and 88%, respectively, with 100% of ATP predictions falling within the 95% confidence interval (compared to 16% for the original model). Finally, we implemented a continuous integration/continuous deployment pipeline for automated updates from future Human-GEM releases. These improvements provide a computationally efficient, well-annotated model for studying central metabolism across human cell types. Availability and ImplementationAll source code for reproducing results from this paper is available at https://doi.org/10.5281/zenodo.20813825.

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biorxiv v2 2026-07-09 source ↗

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