Citations

GECKO 3

Chen, Y., Gustafsson, J., Tafur Rangel, A., Anton, M., Domenzain, I., Kittikunapong, C., Li, F., Yuan, L., Nielsen, J. & Kerkhoven, E. J. (2024). Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0. Nature Protocols 19: 629-667. https://doi.org/10.1038/s41596-023-00931-7

DLKcat

Li, F. et al. (2022). Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nature Catalysis 5: 662-672. https://doi.org/10.1038/s41929-022-00798-z

GECKO 2

Domenzain, I. et al. (2022). Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nature Communications 13: 3766. https://doi.org/10.1038/s41467-022-31421-1

GECKO 1

Sanchez, B. J. et al. (2017). Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Molecular Systems Biology 13: 935. https://doi.org/10.15252/msb.20167411

Code and data availability

The source code of the GECKO toolbox is publicly available under the MIT license at CompMetEng/GECKO, also archived at https://doi.org/10.5281/zenodo.7699818. All limited data used in the tutorials are distributed as part of the code.