PT  - JOURNAL ARTICLE
AU  - Iván Domenzain
AU  - Benjamín Sánchez
AU  - Mihail Anton
AU  - Eduard J. Kerkhoven
AU  - Aarón Millán-Oropeza
AU  - Céline Henry
AU  - Verena Siewers
AU  - John P. Morrissey
AU  - Nikolaus Sonnenschein
AU  - Jens Nielsen
TI  - Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
AID  - 10.1101/2021.03.05.433259
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.05.433259
4099  - http://biorxiv.org/content/early/2021/03/05/2021.03.05.433259.short
4100  - http://biorxiv.org/content/early/2021/03/05/2021.03.05.433259.full
AB  - Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.Competing Interest StatementThe authors have declared no competing interest.