Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints
Abstract Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinet...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2017-08-01
|
| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20167411 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849760954811678720 |
|---|---|
| author | Benjamín J Sánchez Cheng Zhang Avlant Nilsson Petri‐Jaan Lahtvee Eduard J Kerkhoven Jens Nielsen |
| author_facet | Benjamín J Sánchez Cheng Zhang Avlant Nilsson Petri‐Jaan Lahtvee Eduard J Kerkhoven Jens Nielsen |
| author_sort | Benjamín J Sánchez |
| collection | DOAJ |
| description | Abstract Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering. |
| format | Article |
| id | doaj-art-2104f48007de4f02904b64ab55859a4b |
| institution | DOAJ |
| issn | 1744-4292 |
| language | English |
| publishDate | 2017-08-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-2104f48007de4f02904b64ab55859a4b2025-08-20T03:06:10ZengSpringer NatureMolecular Systems Biology1744-42922017-08-0113811610.15252/msb.20167411Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraintsBenjamín J Sánchez0Cheng Zhang1Avlant Nilsson2Petri‐Jaan Lahtvee3Eduard J Kerkhoven4Jens Nielsen5Department of Biology and Biological Engineering, Chalmers University of TechnologyScience for Life Laboratory, KTH – Royal Institute of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyDepartment of Biology and Biological Engineering, Chalmers University of TechnologyAbstract Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering.https://doi.org/10.15252/msb.20167411enzyme kineticsflux balance analysismolecular crowdingproteomicsSaccharomyces cerevisiae |
| spellingShingle | Benjamín J Sánchez Cheng Zhang Avlant Nilsson Petri‐Jaan Lahtvee Eduard J Kerkhoven Jens Nielsen Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints Molecular Systems Biology enzyme kinetics flux balance analysis molecular crowding proteomics Saccharomyces cerevisiae |
| title | Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints |
| title_full | Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints |
| title_fullStr | Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints |
| title_full_unstemmed | Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints |
| title_short | Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints |
| title_sort | improving the phenotype predictions of a yeast genome scale metabolic model by incorporating enzymatic constraints |
| topic | enzyme kinetics flux balance analysis molecular crowding proteomics Saccharomyces cerevisiae |
| url | https://doi.org/10.15252/msb.20167411 |
| work_keys_str_mv | AT benjaminjsanchez improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints AT chengzhang improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints AT avlantnilsson improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints AT petrijaanlahtvee improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints AT eduardjkerkhoven improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints AT jensnielsen improvingthephenotypepredictionsofayeastgenomescalemetabolicmodelbyincorporatingenzymaticconstraints |