Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0
Journal article, 2024
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.
Author
Yu Chen
Shenzhen Institute of Advanced Technology
Chalmers, Life Sciences, Systems and Synthetic Biology
Johan Gustafsson
Chalmers, Biology and Biological Engineering
Albert Tafur Rangel
Novo Nordisk Foundation
Petre Mihail Anton
Chalmers, Life Sciences, Systems and Synthetic Biology
Iván Domenzain Del Castillo Cerecer
Chalmers, Life Sciences, Systems and Synthetic Biology
Cheewin Kittikunapong
Chalmers, Life Sciences, Systems and Synthetic Biology
Feiran Li
Tsinghua University
Chalmers, Life Sciences, Systems and Synthetic Biology
Le Yuan
Chalmers, Life Sciences, Systems and Synthetic Biology
Jens B Nielsen
BioInnovation Institute
Chalmers, Life Sciences, Systems and Synthetic Biology
Eduard Kerkhoven
Chalmers, Life Sciences, Systems and Synthetic Biology
Novo Nordisk Foundation
Nature Protocols
1754-2189 (ISSN) 17502799 (eISSN)
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Subject Categories
Biochemistry and Molecular Biology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1038/s41596-023-00931-7
PubMed
38238583