ArticleNature communications2023
Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Low-DimensionalMolecules (Basel, Switzerland) · 2026Article
- Target of Rapamycin Coordinates Metabolic Remodeling at the Protein Level in the Red AlgaPlants (Basel, Switzerland) · 2026Article
- SAGA1 and SAGA2 localize the starch sheath to the pyrenoid inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Synthetic Biology and Metabolic Engineering of Microalgae for Sustainable Lipid and Terpenoid Production: An Updated Perspective.Plant biotechnology journal · 2026Review
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- Constraint-based metabolic modeling reveals metabolic properties underpinning the unprecedented growth of Chlorella ohadii.The New phytologist · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Charting the state of GEMs in microalgae: progress, challenges, and innovations.Frontiers in plant science · 2025Review
- Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.PLoS computational biology · 2024Article
- Review
- Integration of proteomic data with genome-scale metabolic models: A methodological overview.Protein science : a publication of the Protein Society · 2024Review
- Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.NAR genomics and bioinformatics · 2024Article
- Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kMicrobial cell factories · 2024Article
- Review
- Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.Nature communications · 2023Article
- Data integration across conditions improves turnover number estimates and metabolic predictions.Nature communications · 2023Article
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Authors and funding
6 authors.
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Abstract
Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.
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