ArticleNature communications2021
Bayesian genome scale modelling identifies thermal determinants of yeast metabolism.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 60 citations in OpenAlex.
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- Influence of Drying Temperature on Quality Characteristics and Drying Kinetics ofFoods (Basel, Switzerland) · 2026Article
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana.The New phytologist · 2025Article
- Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- No universal mathematical model for thermal performance curves across traits and taxonomic groups.Nature communications · 2024Article
- Driving towards digital biomanufacturing by CHO genome-scale models.Trends in biotechnology · 2024Review
- Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kMicrobial cell factories · 2024Article
- High temperature delays and low temperature accelerates evolution of a new protein phenotype.Nature communications · 2024Article
- Quantifying thermal adaptation of soil microbial respiration.Nature communications · 2023Article
- Parameter inference for enzyme and temperature constrained genome-scale models.Scientific reports · 2023Article
- Learning deep representations of enzyme thermal adaptation.Protein science : a publication of the Protein Society · 2022Article
- Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.Nature communications · 2022Article
- Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors.Microbial biotechnology · 2022Article
- Strategies to increase tolerance and robustness of industrial microorganisms.Synthetic and systems biotechnology · 2022Article
- Genome-scale modeling of yeast metabolism: retrospectives and perspectives.FEMS yeast research · 2022Review
- Respiratory reoxidation of NADH is a key contributor to high oxygen requirements of oxygen-limited cultures of Ogataea parapolymorpha.FEMS yeast research · 2022Article
- Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.
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