ReviewNature protocols2024
Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.
Review in Nature protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.Bioinformatics (Oxford, England) · 2026Article
- Reimagining Lignin Valorization: Synthetic Biology-Enabled Sustainable Aromatic Carbon Biomanufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- AlphaGEM enables precise genome-scale metabolic modelling by integrating protein structure alignment with deep-learning-based dark metabolism mining.Nature communications · 2026Article
- Model-driven analysis reveals oxidative stress adaptation enabling efficient energy utilization in a Crabtree-negative Saccharomyces cerevisiae.Scientific reports · 2026Article
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 2026Article
- Enzyme-constrained genome-scale model of Yarrowia lipolytica predicts growth-phase specific metabolic engineering targets.Applied microbiology and biotechnology · 2026Article
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- Advances in genome-scale metabolic modeling of Bacillus subtilis.Biotechnology letters · 2026Review
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- COBRA-k: A powerful framework bridging constraint-based and kinetic metabolic modeling.Science advances · 2026Article
- Genome-scale Metabolic Modeling Guided Escherichia coli Engineering for De Novo Biosynthesis of Chrysanthemic Acid.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Genome-scale metabolic modelling of human gut microbes to inform rational community design.Gut microbes · 2025Article
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions.NPJ systems biology and applications · 2025Article
- Genome-scale metabolic modeling ofmSystems · 2025Article
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.