Zieringer Julia, Takors Ralf
Institute of Biochemical Engineering, University of Stuttgart, Germany.
Comput Struct Biotechnol J. 2018 Jul 6;16:246-256. doi: 10.1016/j.csbj.2018.06.002. eCollection 2018.
Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.
工业生物反应器的容积从10000升到700000升不等，其特点是显示出不同的底物可利用区、溶解气体浓度区和pH值区，这反映了放大过程中的物理、技术和经济限制。微生物生产者在生物反应器内波动，因此经常经历不断变化的微环境条件。外部刺激会引发对微生物代谢和转录调控程序的反应。与从理想的、充分混合的实验室规模条件推导的预期相比，这两者都可能使大规模预期的微生物生产性能下降。因此，需要预测工具来量化考虑生物反应器异质性的大规模影响。该综述表明，将微生物动力学模拟与大规模环境条件计算相结合以预测生物反应器性能的时机已经成熟。因此，本文介绍了基本的实验程序和计算工具，以推导合适的微生物模型和流体动力学条件，并将两者联系起来进行生物反应器建模。特别强调了基因调控网络的识别，因为此类模型的实施肯定会在未来的研究中获得发展动力。
