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

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Abstract

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.

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Acknowledgements

The authors acknowledge the financial support received from Junta de Andalucía, Spain (P11-TEP-7737).

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Authors and Affiliations

  1. Chemical Engineering Area, University of Almería, 04120, Almería, Spain

    Antonio Contreras-Gómez, Alba Beas-Catena, Asterio Sánchez-Mirón, Francisco García-Camacho & Emilio Molina Grima

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  1. Antonio Contreras-Gómez
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  2. Alba Beas-Catena
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  3. Asterio Sánchez-Mirón
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  4. Francisco García-Camacho
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  5. Emilio Molina Grima
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Correspondence to Asterio Sánchez-Mirón.

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Contreras-Gómez, A., Beas-Catena, A., Sánchez-Mirón, A. et al. The use of an artificial neural network to model the infection strategy for baculovirus production in suspended insect cell cultures. Cytotechnology 70, 555–565 (2018). https://doi.org/10.1007/s10616-017-0128-x

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  • DOI: https://doi.org/10.1007/s10616-017-0128-x

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