, Volume 70, Issue 2, pp 555–565 | Cite as

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

  • Antonio Contreras-Gómez
  • Alba Beas-Catena
  • Asterio Sánchez-MirónEmail author
  • Francisco García-Camacho
  • Emilio Molina Grima
Original Article


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.


Baculovirus Infection strategy Modeling Neural network 



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


  1. Aehle M, Simutis R, Lübbert A (2010) Comparison of viable cell concentration estimation methods for mammalian cell cultivation process. Cytotechnology 62:413–422CrossRefGoogle Scholar
  2. Agathos SN (2010) Insect cell culture. In: Baltz RH, Davies JE, Demain AL (eds) Manual of industrial microbiol and biotechnol. ASM Press, Washington, pp 212–222Google Scholar
  3. Ahmadi H, Golian A (2010) The integration of broiler chicken threonine responses data into neural network models. Poult Sci 89:2535–2541CrossRefGoogle Scholar
  4. Baş D, Boyaci IH (2007) Modeling and optimization I: usability of response surface methodology. J Food Eng 78:836–845CrossRefGoogle Scholar
  5. Beas-Catena A, Sánchez-Mirón A, García-Camacho F, Molina-Grima E (2011) Adaptation of the Se301 insect cell line to suspension culture. Effect of turbulence on growth and on production of nucleopolyhedrovirus (SeMNPV). Cytotechnology 63:543–552CrossRefGoogle Scholar
  6. Beas-Catena A, Sánchez-Mirón A, García-Camacho F, Contreras-Gómez A, Molina- Grima E (2013a) Adaptation of the Spodoptera exigua Se301 insect cell line to grow in serum-free suspended culture. Comparison of SeMNPV productivity in serum-free and serum-containing media. Appl Microbiol Biotechnol 97:3373–3381CrossRefGoogle Scholar
  7. Beas-Catena A, Sánchez-Mirón A, García-Camacho F, Contreras-Gómez A, Molina-Grima E (2013b) The effect of spent medium recycle on cell proliferation, metabolism and baculovirus production by the lepidopteran Se301 cell line infected at very low MOI. J Microbiol Biotechnol 23:1747–1756CrossRefGoogle Scholar
  8. Carinhas N, Bernal V, Yokomizo AY, Carrondo MJT, Oliveira R, Alves PM (2009) Baculovirus production for gene therapy: the role of cell density, multiplicity of infection and medium exchange. Appl Microbiol Biotechnol 81:1041–1049CrossRefGoogle Scholar
  9. Chan LC, Greenfield PF, Reid S (1998) Optimising fed-batch production of recombinant proteins using the baculovirus expression vector system. Biotechnol Bioeng 59:178–188CrossRefGoogle Scholar
  10. Chang KH, Zandstra PW (2004) Quantitative screening of embryonic stem cell differentiation: endoderm formation as a model. Biotechnol Bioeng 88:287–298CrossRefGoogle Scholar
  11. Contreras-Gómez A, Sánchez-Mirón A, García-Camacho F, Molina-Grima E, Chisti Y (2014) Protein production using the baculovirus-insect cell expression system. Biotechnol Progress 30:1–18CrossRefGoogle Scholar
  12. Dudognon B, Romero-Santacreu L, Gómez-Sebastián S, Hidalgo AB, López-Vidal J, Bellido ML, Muñoz E, Escribano JM (2014) Production of functional active human growth factors in insects used as living biofactories. J Biotechnol 184:229–239CrossRefGoogle Scholar
  13. García-Camacho F, Gallardo-Rodríguez JJ, Sánchez-Mirón A, Cerón-García MC, Belarbi EH, Molina-Grima E (2007) Determination of shear stress thresholds in toxic dinoflagellates cultured in shaken flasks: implications in bioprocess engineering. Process Biochem 42:1506–1515CrossRefGoogle Scholar
  14. García-Camacho F, López-Rosales L, Sánchez-Mirón A, Belarbi EH, Chisti Y, Molina-Grima E (2016) Artificial neural network modeling for predicting the growth of the microalga Karlodinium veneficum. Algal Res 14:58–64CrossRefGoogle Scholar
  15. Geeraerd AH, Herremans CH, Cenens C, van Impe JF (1998) Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products. Int J Food Microbiol 44:49–68CrossRefGoogle Scholar
  16. Hitchman RB, Locanto E, Possee RD, King LA (2011) Optimizing the baculovirus expression vector system. Methods 55:52–57CrossRefGoogle Scholar
  17. King LA, Possee RD (1992) The baculovirus expression system. Chapman & Hall, LondonCrossRefGoogle Scholar
  18. Korany MA, Mahgoub H, Fahmy OT, Maher HM (2012) Application of artificial neural networks for response surface modelling in HPLC method development. J Adv Res 3:53–63CrossRefGoogle Scholar
  19. López-Rosales L, Gallardo-Rodríguez JJ, Sánchez-Mirón A, Contreras-Gómez A, García-Camacho F, Molina-Grima E (2013) Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks. Bioresour Technol 146:682–688CrossRefGoogle Scholar
  20. Maier HR, Dandy GC (2001) Neural network based modelling of environmental variables: a systematic approach. Math Comput Model 33:669–682CrossRefGoogle Scholar
  21. Mandenius CF, Brundin A (2008) Biocatalysts and bioreactor design. Biotechnol Prog 24:1191–1203CrossRefGoogle Scholar
  22. Marique T, Cherlet M, Hendick V, Godia F, Kretzmer G, Wérenne J (2001) A general artificial neural network for the modelization of culture kinetics of different CHO strains. Cytotechnology 36:55–60CrossRefGoogle Scholar
  23. Mena JA, Kamen AA (2011) Insect cell technology is a versatile and robust vaccine manufacturing platform. Expert Rev Vaccines 10:1063–1081CrossRefGoogle Scholar
  24. Micheloud GA, Gioria VV, Pérez G, Claus JD (2009) Production of occlusion bodies of Anticarsia gemmatalis multiple nucleopolyhedrovirus in serum-free suspension cultures of the saUFL-AG-286 cell line: influence of infection conditions and statistical optimization. J Virol Methods 162:258–266CrossRefGoogle Scholar
  25. Ohki T, Mikhailenko SV, Arai T, Ishii S, Ishiwata SI (2012) Improvement of the yields of recombinant actin and myosin V-HMM in the insect cell/baculovirus system by the addition of nutrients to the high-density cell culture. J Muscle Res Cell Motil 33:351–358CrossRefGoogle Scholar
  26. Rausch M, Pörtner R, Knäblein J (2013) Increase of protein yield in high five cells in a single-use perfusion bioreactor by medium replacement. Chem Eng Technol 85:111–117Google Scholar
  27. Roldão A, Vieira HL, Charpilienne A, Poncet D, Roy P, Carrondo MJ, Alves PM, Oliveira R (2007) Modeling rotavirus-like particles production in a baculovirus expression vector system: infection kinetics, baculovirus DNA replication, mRNA synthesis and protein production. J Biotechnol 128:875–894CrossRefGoogle Scholar
  28. Sävenhed J (2001) Optimizing the production of glucocorticoid receptor in insect cell-baculovirus expression system using response surface analysis. Dissertation, Linköping University, SwedenGoogle Scholar
  29. Silva R, Ferreira S, Bonifácio MJ, Dias JML, Queiroz JA, Passarinha LA (2012) Optimization of fermentation conditions for the production of human soluble catechol-O-methyltransferase by Escherichia coli using artificial neural network. J Biotechnol 160:161–168CrossRefGoogle Scholar
  30. Tokatli F, Tari C, Unluturk SM, Baysal NG (2009) Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC20235. J Ind Microbiol Biotechnol 36:1139–1148CrossRefGoogle Scholar
  31. Valletta E, Kučera L, Prokeš L, Amato F, Pivetta T, Hampl A, Havel J, Vaňhara P (2016) Multivariate calibration approach for quantitative determination of cell-line cross contamination by intact cell mass spectrometry and artificial neural networks. PLoS ONE 11:e0147414CrossRefGoogle Scholar
  32. van Oers MM (2011) Opportunities and challenges for the baculovirus expression system. J Invertebr Pathol 107:S3–S15CrossRefGoogle Scholar
  33. van Oers MM, Lynn DE (2010) Insect cell culture. In: Encyclopedia of life sciences. Wiley, Chichester. doi:  10.1002/9780470015902.a0002574.pub2
  34. Zhang YH, Enden G, Merchuk JC (2005) Insect cells–baculovirus system: factors 418 affecting growth and low MOI infection. Biochem Eng J 27:8–16CrossRefGoogle Scholar
  35. Zhou G, Zhang Y, Ke Y (2011) Optimization of the strategy for recombinant baculovirus infection of suspended insect cells. In: Stoytcheva M (ed) Pesticides in the modern world-pests control and pesticides exposure and toxicity assessment. Intech, New York. ISBN: 978-953-307-457-3. Accessed 15 Sept 2016

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Chemical Engineering Area, University of AlmeríaAlmeríaSpain

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