Hemmerich Johannes, Freier Lars, Wiechert Wolfgang, von Lieres Eric, Oldiges Marco
IBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC).
IBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC); Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University.
J Vis Exp. 2017 Dec 15(130):56234. doi: 10.3791/56234.
A core business in industrial biotechnology using microbial production cell factories is the iterative process of strain engineering and optimization of bioprocess conditions. One important aspect is the improvement of cultivation medium to provide an optimal environment for microbial formation of the product of interest. It is well accepted that the media composition can dramatically influence overall bioprocess performance. Nutrition medium optimization is known to improve recombinant protein production with microbial systems and thus, this is a rewarding step in bioprocess development. However, very often standard media recipes are taken from literature, since tailor-made design of the cultivation medium is a tedious task that demands microbioreactor technology for sufficient cultivation throughput, fast product analytics, as well as support by lab robotics to enable reliability in liquid handling steps. Furthermore, advanced mathematical methods are required for rationally analyzing measurement data and efficiently designing parallel experiments such as to achieve optimal information content. The generic nature of the presented protocol allows for easy adaption to different lab equipment, other expression hosts, and target proteins of interest, as well as further bioprocess parameters. Moreover, other optimization objectives like protein production rate, specific yield, or product quality can be chosen to fit the scope of other optimization studies. The applied Kriging Toolbox (KriKit) is a general tool for Design of Experiments (DOE) that contributes to improved holistic bioprocess optimization. It also supports multi-objective optimization which can be important in optimizing both upstream and downstream processes.
利用微生物生产细胞工厂的工业生物技术的核心业务是菌株工程和生物工艺条件优化的迭代过程。一个重要方面是改进培养基，为目标产物的微生物合成提供最佳环境。众所周知，培养基组成会极大地影响整个生物工艺的性能。营养培养基优化有助于提高微生物系统中重组蛋白的产量，因此，这是生物工艺开发中值得采取的一步。然而，标准培养基配方往往取自文献，因为定制设计培养基是一项繁琐的任务，需要微生物反应器技术以实现足够的培养通量、快速的产物分析，以及实验室机器人技术的支持，以确保液体处理步骤的可靠性。此外，还需要先进的数学方法来合理分析测量数据并高效设计平行实验，以获得最佳信息量。所提出方案的通用性使得它能够轻松适应不同的实验室设备、其他表达宿主、目标蛋白，以及其他生物工艺参数。此外，可以选择其他优化目标，如蛋白质生产率、比产率或产品质量，以适应其他优化研究的范围。所应用的克里金工具箱（KriKit）是一种用于实验设计（DOE）的通用工具，有助于改进整体生物工艺优化。它还支持多目标优化，这在优化上游和下游工艺时可能很重要。
