ArticleBiotechnology for biofuels and bioproducts2024
Heat of reaction in individual metabolic pathways of yeast determined by mechanistic modeling in an insulated bioreactor.
Article in Biotechnology for biofuels and bioproducts, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Thermotolerant yeasts promoting climate-resilient bioproduction.FEMS yeast research · 2025Review
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3 authors.
Funding
Abstract
backgroundThe yeast Saccharomyces cerevisiae, commonly used in industry, exhibits complex metabolism due to the Crabtree effect, fermenting alcohol even under aerobic conditions when glucose exceeds 0.10-0.15 g/L. The heat released by the biological activity is a signal very easy to collect, given the minimal instrumentation requirements. However, this heat depends on the activated metabolic pathways and provides only an indirect indicator, that cannot be used in a simple way. This study demonstrated the potential of a mechanistic model to control the process by measuring the heat released by the biological activity.
resultsThe complexity arising from coexisting metabolic pathways was addressed by a comprehensive model of Saccharomyces cerevisiae together with the heat of reaction included in a rigorous enthalpy balance of the bioreactor. Batch cultures were performed in an insulated bioreactor to trigger a temperature signal. The heat of individual metabolic pathways was determined by inverse analysis of these tests using Particle Swarm Optimization (PSO): -101.28 ±0.02kJ/mol for anaerobic fermentation, -231.27±0.06kJ/mol for aerobic fermentation, and -662.94 ± 0.54kJ/mol for ethanol respiration. Finally, the model was successfully applied and validated for online training under different operating conditions.
conclusionsThe model demonstrates remarkable accuracy, with a mean relative error under 0.38% in temperature predictions for both anaerobic and aerobic conditions. The viscous dissipation is a key parameter specific to the bioreactor and the growth conditions. However, we demonstrated that this parameter could be fitted accurately from the early stages of the experiment for further prediction of the remaining part. This model introduces temperature, or the thermal power required to maintain temperature, as a measurable parameter for online feedback model training to provide increasingly precise feed-forward control.
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