ArticleBMC systems biology2011
Expanding a dynamic flux balance model of yeast fermentation to genome-scale.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Dynamic, model-guided intensification of fuel ethanol production in yeast: proteome constraints, CRISPR rewiring, and high-gravity fermentations.Archives of microbiology · 2026Review
- An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of themSystems · 2025Article
- Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway.Microbial biotechnology · 2023Article
- Consideration of Maintenance in Wine Fermentation Modeling.Foods (Basel, Switzerland) · 2022Article
- Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts.Microbial cell factories · 2021Article
- The Monod Model Is Insufficient To Explain Biomass Growth in Nitrogen-Limited Yeast Fermentation.Applied and environmental microbiology · 2021Article
- A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of themSystems · 2021Article
- QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism.Scientific reports · 2020Article
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- Engineering TATA-binding protein Spt15 to improve ethanol tolerance and production inBiotechnology for biofuels · 2018Article
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- Management of Multiple Nitrogen Sources during Wine Fermentation by Saccharomyces cerevisiae.Applied and environmental microbiology · 2017Article
- Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris.BMC systems biology · 2017Article
- Genome-Scale Reconstruction of the Metabolic Network inFrontiers in microbiology · 2017Article
- Analyzing redox balance in a synthetic yeast platform to improve utilization of brown macroalgae as feedstock.Metabolic engineering communications · 2015Article
- Use of chemostat cultures mimicking different phases of wine fermentations as a tool for quantitative physiological analysis.Microbial cell factories · 2014Article
- Age-correlated stress resistance improves fitness of yeast: support from agent-based simulations.BMC systems biology · 2014Article
- Predictive sulfur metabolism - a field in flux.Frontiers in plant science · 2014Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundYeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints.
resultsAppropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol.
conclusionA useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.