|Publisher DOI:||10.1007/978-1-0716-0191-4_13||Title:||Efficient optimization of process strategies with model-assisted design of experiments||Language:||English||Authors:||Kuchemüller, Kim Beatrice
|Keywords:||Batch;Computer-aided methods;DoE;Experimental space;Fed-batch;Response surface||Issue Date:||2020||Source:||Methods in Molecular Biology (2095): 235-249 (2020)||Journal:||Methods in molecular biology||Abstract (english):||
Conventional design of experiments (DoE) methods require expert knowledge about the investigated factors and their boundary values and mostly lead to multiple rounds of time-consuming and costly experiments. The combination of DoE with mathematical process modeling in model-assisted DoE (mDoE) can be used to increase the mechanistic understanding of the process. Furthermore, it is aimed to optimize the processes with respect to a target (e.g., amount of cells, product titer), which also provides new insights into the process. In this chapter, the workflow of mDoE is explained stepwise including corresponding protocols. Firstly, a mathematical process model is adapted to cultivation data of first experimental data or existing knowledge. Secondly, model-assisted simulations are treated in the same way as experimentally derived data and included as responses in statistical DoEs. The DoEs are then evaluated based on the simulated data, and a constrained-based optimization of the experimental space can be conducted. This loop can be repeated several times and significantly reduces the number of experiments in process development.
|URI:||http://hdl.handle.net/11420/4614||ISSN:||1064-3745||Institute:||Bioprozess- und Biosystemtechnik V-1||Document Type:||Chapter (Book)||Project:||IBÖM04:mDoE-Toolbox2-Neue mDoE Software-Toolbox zur modellgestützten Optimierung biotechnologischer Prozesse|
|Appears in Collections:||Publications without fulltext|
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