Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
Journal article, 2022
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 such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are 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 is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
Author
Iván Domenzain Del Castillo Cerecer
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
Benjamín José Sánchez
Technical University of Denmark (DTU)
Petre Mihail Anton
Science for Life Laboratory (SciLifeLab)
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Eduard Kerkhoven
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
Aaron Millan-Oropeza
University Paris-Saclay
Céline Henry
University Paris-Saclay
Verena Siewers
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
John P. Morrissey
APC Microbiome Ireland
N. Sonnenschein
Technical University of Denmark (DTU)
Jens B Nielsen
Novo Nordisk Foundation
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
BioInnovation Institute
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 13 1 3766Subject Categories
Biochemistry and Molecular Biology
Bioinformatics (Computational Biology)
DOI
10.1038/s41467-022-31421-1
PubMed
35773252