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Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori.
Biotechnology and Bioengineering ( IF 3.8 ) Pub Date : 2020-06-12 , DOI: 10.1002/bit.27457
Maike Kuschel 1 , Ralf Takors 1
Affiliation  

Transferring bioprocesses from lab to industrial scale without loss of performance is key for the successful implementation of novel production approaches. Because mixing and mass transfer is usually hampered in large scale, cells experience heterogeneities eventually causing deteriorated yields, that is, reduced titers, productivities, and sugar‐to‐product conversions. Accordingly, reliable and easy‐to‐implement tools for a priori prediction of large‐scale performance based on dry and wet‐lab tests are heavily needed. This study makes use of computational fluid dynamic simulations of a multiphase multi‐impeller stirred tank in pilot scale. So‐called lifelines, records of 120,000 Corynebacterium glutamicum cells experiencing fluctuating environmental conditions, were identified and used to properly design wet‐lab scale‐down (SD) devices. Physical parameters such as power input, gas hold up, urn:x-wiley:00063592:media:bit27457:bit27457-math-0001, and mixing time showed good agreement with experimental measurements. Analyzing the late fed‐batch cultivation revealed that the complex double gradient of glucose and oxygen can be translated into a wet‐lab SD setup with only few compartments. Most remarkably, the comparison of different mesh sizes outlined that even the coarsest approach with a mesh density of urn:x-wiley:00063592:media:bit27457:bit27457-math-0002 was sufficient to properly predict physical and biological readouts. Accordingly, the approach offers the potential for the thorough analysis of realistic industrial case scenarios.

中文翻译:

模拟氧气和葡萄糖梯度作为先验预测工业规模性能的先决条件。

在不损失性能的情况下将生物过程从实验室转移到工业规模是成功实施新型生产方法的关键。由于混合和传质通常在大规模中受到阻碍,细胞会经历异质​​性,最终导致产量下降,即滴度、生产率和糖转化为产品的转化率降低。因此,迫切需要可靠且易于实施的工具,用于基于干湿实验室测试对大规模性能进行先验预测。本研究利用中试规模的多相多叶轮搅拌罐的计算流体动力学模拟。所谓的生命线,12万条谷氨酸棒杆菌的记录经历波动的环境条件的细胞被识别并用于正确设计湿实验室缩小(SD)设备。诸如功率输入、气体滞留量urn:x-wiley:00063592:media:bit27457:bit27457-math-0001和混合时间等物理参数与实验测量结果非常吻合。对后期补料分批培养的分析表明,葡萄糖和氧气的复杂双梯度可以转化为只有几个隔室的湿实验室 SD 设置。最值得注意的是,不同网格尺寸的比较表明,即使是网格密度为 0.5 的最粗略的方法urn:x-wiley:00063592:media:bit27457:bit27457-math-0002也足以正确预测物理和生物读数。因此,该方法提供了对现实工业案例场景进行彻底分析的潜力。
更新日期:2020-08-14
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