Siemens AG, Corporate Technology, Siemensstraße 90, Vienna A-1210, Austria.
Institute for Mechanics of Materials and Structures, Vienna University of Technology, Karlsplatz 13/202, Vienna A-1040, Austria.
J Theor Biol. 2018 Sep 14;453:125-135. doi: 10.1016/j.jtbi.2018.05.016. Epub 2018 May 17.
A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations.
提出了一种结合实验/理论的方法，用于提高酿酒酵母发酵的可预测性。特别是，开发了一个数学模型，明确考虑了发酵过程的主要机制，允许连续计算关键过程变量，包括生物量浓度和呼吸商（RQ）。为了模型校准和实验验证，进行了分批和补料分批发酵。将模型预测的生物量浓度和 RQ 发展与相应的实验记录值进行比较，表明分批和补料分批过程都具有非常好的一致性，证实了模型的充分性。此外，还进行了敏感性研究，以确定对模型预测有显著影响的模型参数：我们的模型对仅六个参数的变化响应具有显著的敏感性。这些研究为模型简化提供了有价值的基础，本文也对此进行了论证。最后，基于优化的参数研究表明，我们的模型如何用于提高酿酒酵母发酵的效率。
