Sawatzki Annina, Hans Sebastian, Narayanan Harini, Haby Benjamin, Krausch Niels, Sokolov Michael, Glauche Florian, Riedel Sebastian L, Neubauer Peter, Cruz Bournazou Mariano Nicolas
Department of Bioprocess Engineering, Department of Biotechnology, Technische Universität Berlin, Ackerstr. 71-76, ACK24, D-13355 Berlin, Germany.
ETH Zürich, Rämistrasse 101, CH-8092 Zurich, Switzerland.
Bioengineering (Basel). 2018 Nov 21;5(4):101. doi: 10.3390/bioengineering5040101.
Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations are a promising alternative to accelerate and optimize bioprocess development allowing for sophisticated cultivation experiments in high throughput. These include fed-batch and continuous cultivations with multiple options of process control and sample analysis which deliver valuable screening tools for industrial production. However, the model-based methods needed to operate these robotic facilities efficiently considering the complexity of biological processes are missing. We present an automated experiment facility that integrates online data handling, visualization and treatment using multivariate analysis approaches to design and operate dynamical experimental campaigns in up to 48 mini-bioreactors (8⁻12 mL) in parallel. In this study, the characterization of AH22 secreting recombinant endopolygalacturonase is performed, running and comparing 16 experimental conditions in triplicate. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable to different strains and cultivation strategies, and suitable for automatized process development reducing the experimental times and costs.
能够实现众多平行培养自动化操作的微型生物反应器系统，是加速和优化生物工艺开发的一种有前景的替代方案，可实现高通量的复杂培养实验。这些系统包括分批补料培养和连续培养，具备多种过程控制和样品分析选项，为工业生产提供了有价值的筛选工具。然而，考虑到生物过程的复杂性，有效操作这些机器人设施所需的基于模型的方法尚不存在。我们展示了一种自动化实验设施，该设施集成了在线数据处理、可视化和使用多变量分析方法进行处理，以设计和操作多达48个微型生物反应器（8⁻12毫升）并行的动态实验活动。在本研究中，对分泌重组内切聚半乳糖醛酸酶的AH22进行了表征，对16种实验条件进行了一式三份的运行和比较。开发了数据驱动的多变量方法，以实现快速、自动决策以及关于内切聚半乳糖醛酸酶生产的在线预测数据分析。利用动态过程信息，通过主成分分析可以检测到行为异常的培养，以及两组行为相似的培养，随后根据进料速率进行分类。通过决策树分析，可以自动识别导致最佳重组产物形成的培养条件。所开发的方法易于适应不同的菌株和培养策略，适用于自动化工艺开发，可减少实验时间和成本。
