Jenzsch Marco, Simutis Rimvydas, Eisbrenner Günter, Stückrath Ingolf, Lübbert Andreas
Institute of Bioengineering, Martin-Luther-University Halle-Wittenberg, 06120, Halle/Saale, Germany.
Bioprocess Biosyst Eng. 2006 Jun;29(1):19-27. doi: 10.1007/s00449-006-0051-6. Epub 2006 Feb 25.
Online biomass estimation for bioprocess supervision and control purposes is addressed. As the biomass concentration cannot be measured online during the production to sufficient accuracy, indirect measurement techniques are required. Here we compare several possibilities for the concrete case of recombinant protein production with genetically modified Escherichia coli bacteria and perform a ranking. At normal process operation, the best estimates can be obtained with artificial neural networks (ANNs). When they cannot be employed, statistical correlation techniques can be used such as multivariate regression techniques. Simple model-based techniques, e.g., those based on the Luedeking/Piret-type are not as accurate as the ANN approach; however, they are very robust. Techniques based on principal component analysis can be used to recognize abnormal cultivation behavior. For the cases investigated, a complete ranking list of the methods is given in terms of the root-mean-square error of the estimates. All techniques examined are in line with the recommendations expressed in the process analytical technology (PAT)-initiative of the FDA.
本文探讨了用于生物过程监测和控制目的的在线生物量估计。由于在生产过程中无法在线测量生物量浓度以达到足够的精度，因此需要间接测量技术。在此，我们针对利用基因改造的大肠杆菌生产重组蛋白的具体案例，比较了几种可能性并进行了排名。在正常工艺操作中，使用人工神经网络（ANN）可获得最佳估计值。当无法使用人工神经网络时，可以采用统计相关技术，如多元回归技术。基于简单模型的技术，例如基于Luedeking/Piret型的技术，不如人工神经网络方法准确；然而，它们非常稳健。基于主成分分析的技术可用于识别异常培养行为。对于所研究的案例，根据估计值的均方根误差给出了方法的完整排名列表。所有研究的技术均符合美国食品药品监督管理局（FDA）的过程分析技术（PAT）倡议中提出的建议。
