González-Hernández Yusmel, Michiels Emilie, Perré Patrick
Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres, Pomacle, 51110, France.
Biotechnol Biofuels Bioprod. 2024 Nov 25;17(1):137. doi: 10.1186/s13068-024-02580-8.
The 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.
The 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.
The 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.
工业上常用的酿酒酵母由于存在克拉布特里效应,代谢过程复杂,即使在有氧条件下,当葡萄糖浓度超过0.10 - 0.15 g/L时也会发酵产生酒精。鉴于所需仪器设备极少,生物活性释放的热量是一个很容易收集的信号。然而,这种热量取决于激活的代谢途径,只是一个间接指标,不能简单地使用。本研究通过测量生物活性释放的热量,证明了机理模型在控制该过程方面的潜力。
通过酿酒酵母的综合模型以及生物反应器严格焓平衡中包含的反应热,解决了共存代谢途径产生的复杂性问题。在隔热的生物反应器中进行分批培养以触发温度信号。使用粒子群优化算法(PSO)对这些试验进行逆分析,确定了各个代谢途径的反应热:厌氧发酵为-101.28±0.02kJ/mol,有氧发酵为-231.27±0.06kJ/mol,乙醇呼吸为-662.94±0.54kJ/mol。最后,该模型在不同操作条件下成功应用并验证了在线训练。
该模型显示出显著的准确性,在厌氧和好氧条件下温度预测的平均相对误差均低于0.38%。粘性耗散是生物反应器和生长条件特有的关键参数。然而,我们证明该参数可以在实验早期准确拟合,以便进一步预测其余部分。该模型引入温度或维持温度所需的热功率作为可测量参数,用于在线反馈模型训练,以提供越来越精确的前馈控制。