Dynamic Optimization of Bioprocesses

DOI: 10.4236/am.2012.330208   PDF   HTML     5,561 Downloads   8,166 Views   Citations

Abstract

The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins with human-like post-translational modifications, is an expensive and labor-intensive process, as animal cells have complex nutrient requirements. Optimization studies have typically been limited to experimental studies, although there has recently been increased interest in combined experimental and computational approaches. In this work, we present the results of a dynamic optimization approach to improving animal cell bioprocesses. We have based this on a model validated over batch and fed-batch conditions and have examined four possible objective functions. Our results indicate that the maximization of the product concentration or the integral of viable cell concentration over time give equivalent results and can improve the product titer up to 70% over non-optimized fed-batch cultures.

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G. Koumpouras and C. Kontoravdi, "Dynamic Optimization of Bioprocesses," Applied Mathematics, Vol. 3 No. 10A, 2012, pp. 1487-1495. doi: 10.4236/am.2012.330208.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] S. Aggarwal, “What’s Fueling the Biotech Engine— 2010-2011,” Nature Biotechnology, Vol. 29, No. 12, 2011, pp. 1083-1089. doi:10.1038/nbt.2060
[2] R. P. Nolan and K. Lee, “Dynamic Model of CHO Cell Metabolism,” Metabolic Engineering, Vol. 13, No. 1, 2011, pp. 108-124. doi:10.1016/j.ymben.2010.09.003
[3] S. Selvarasu, Y. S. Ho, W. P. K. Chong, N. S. C. Wong, F. N. K. Yusufi, Y. Y. Lee, M. G. S. Yap and D. Y. Lee, “Combined in Silico Modeling and Metabolomics Analysis to Characterize Fed-Batch CHO Cell Culture,” Biotechnology and Bioengineering, Vol. 109, No. 6, 2012, pp. 1415-1429. doi:10.1002/bit.24445
[4] C. Kontoravdi, E. N. Pistikopoulos and A. Mantalaris, “Systematic Development of Predictive Mathematical Models for Animal Cell Cultures,” Computers and Chemical Engineering, Vol. 34, No. 8, 2010, pp. 1192-1198. doi:10.1016/j.compchemeng.2010.03.012
[5] Process Systems Enterprise Ltd., “gPROMS Advanced User Guide,” Process Systems Enterprise Ltd., London, 2011, United Kingdom, http://www.psenterprise.com/
[6] S. L. Chong, D. G. Mou, S. H. Lim, A. Alib and B. T. Tey, “Enhancement of Monoclonal Antibody Productivity by Promoting Active Hypothermic Growth in Hybridoma Cells,” Journal of Chemical Technology and Biotechnology, Vol. 84, No. 11, 2009, pp. 1674-1680. doi:10.1002/jctb.2228
[7] S. J. Hwang, S. K. Yoon, G. Y. Koh and G. M. Lee, “Effects of Culture Temperature and pH on Flag-Tagged COMP Angiopoietin-1 (FCA1) Production from Recombinant CHO Cells: FCA1 Aggregation,” Applied Microbiology and Biotechnology, Vol. 91, No. 2, 2011, pp. 305-315. doi:10.1007/s00253-011-3266-7
[8] Y. Ho, J. Varley, A. Mantalaris, “Development and Analysis of a Mathematical Model for Antibody-Producing GS-NS0 Cells under Normal and Hyperosmotic Culture Conditions,” Biotechnology Progress, Vol. 22, No. 6, 2006, pp. 1560-1569.

  
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