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The use of an artificial neural network to model the infection strategy for baculovirus production in suspended insect cell cultures

机译:使用人工神经网络来模拟悬浮昆虫细胞培养中杆状病毒生产的感染策略

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摘要

Since the infection strategy in the baculovirus-insect cell system mostly affects production of the vector itself or the target product, and given that individual infection parameters interact with each other, the optimal combination must be established for each such specific system. In this work an artificial neural network was used to model infection strategy, including the cell concentration at infection, the multiplicity of infection, the medium recycle, and agitation intensity, and to evaluate the relative importance of each factor in the baculovirus production obtained. The results demonstrate that this model can be used to select an optimal infection strategy. For the baculovirus-insect cell system used in this study, this includes low multiplicity of infection and agitation intensity, along with high cell concentration at infection and medium recycle. Our model is superior to regression methods and predicts baculovirus production more precisely, thus meaning that it could be useful for the development of feasible processes, thereby improving process performance and economy.
机译:由于杆状病毒-昆虫细胞系统中的感染策略主要影响载体本身或目标产物的产生,并且考虑到各个感染参数相互影响,因此必须为每个这样的特定系统建立最佳组合。在这项工作中,使用了人工神经网络来模拟感染策略,包括感染时的细胞浓度,感染的多样性,培养基的循环以及搅拌强度,并评估每种因素在获得的杆状病毒生产中的相对重要性。结果表明,该模型可用于选择最佳感染策略。对于本研究中使用的杆状病毒-昆虫细胞系统,这包括低感染复数和激动强度,以及感染和培养基回收时细胞浓度高。我们的模型优于回归方法,并且可以更精确地预测杆状病毒的产生,这意味着它对于开发可行的过程可能有用,从而提高了过程的性能和经济性。

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