ArticleMolecular systems biology2023
Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data.
Article in Molecular systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 20 citations in OpenAlex.
- Incorporation of Cryptic Plasmid Energetics Improves Genome-Scale Metabolic Predictions in Probiotic E. coli Nissle 1917.Microbial biotechnology · 2026Article
- Bayesian inference captures metabolite-bacteria interactions in a microbial community.ISME communications · 2026Article
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- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
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- A compact model of Escherichia coli core and biosynthetic metabolism.PLoS computational biology · 2025Article
- Recent trends in microbial production of alkanes.World journal of microbiology & biotechnology · 2025Review
- Data-driven synthetic microbes for sustainable future.NPJ systems biology and applications · 2025Review
- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Emerging tools and best practices for studying gut microbial community metabolism.Nature metabolism · 2024Review
- Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas.Briefings in bioinformatics · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Transporter annotations are holding up progress in metabolic modeling.Frontiers in systems biology · 2024Article
- Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data.Molecular systems biology · 2023Article
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4 authors at 2 institutions in 1 country.
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Abstract
The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
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