Bioprocess and Biosystems Engineering

, Volume 35, Issue 3, pp 333–340 | Cite as

Improving cultivation processes for recombinant protein production

  • A. Kuprijanov
  • S. Schaepe
  • M. Aehle
  • R. Simutis
  • A. Lübbert
Original Paper


An new cascade control system is presented that reproducibly keeps the cultivation part of recombinant protein production processes on its predetermined track. While the system directly controls carbon dioxide production mass and carbon dioxide production rates along their setpoint profiles in fed-batch cultivation, it simultaneously keeps the specific biomass growth rates and the biomass profiles on their desired paths. The control scheme was designed and tuned using a virtual plant environment based on the industrial process control system SIMATIC PCS 7 (Siemens AG). It is shown by means of validation experiments that the simulations in this straightforward approach directly reflect the experimentally observed controller behaviour. Within the virtual plant environment, it was shown that the cascade control is considerably better than previously used control approaches. The controller significantly improved the batch-to-batch reproducibility of the fermentations. Experimental tests confirmed that it is particularly suited for cultivation processes suffering from long response times and delays. The performance of the new controller is demonstrated during its application in Escherichia coli fed-batch cultivations as well as in animal cell cultures with CHO cells. The technique is a simple and reliable alternative to more sophisticate model-supported controllers.


Cascade control Virtual plant Recombinant proteins Batch-to-batch reproducibility 



The financial support from the Ministry of Science and Education by means of the Excellence-Initiative Sachsen-Anhalt is gratefully acknowledged. We also want to thank the Research Council of Lithuania for their financial support.


  1. 1.
    Aehle M, Kuprijanov A, Schaepe S, Simutis R, Lübbert A (2011) Increasing batch-to-batch reproducibility of CHO cultures by robust open-loop control. Cytotechnology 63:41–47CrossRefGoogle Scholar
  2. 2.
    Aehle M, Schaepe S, Kuprijanov A, Simutis R, Lübbert A (2011) Simple and efficient control of CHO cell cultures. J Biotechnol. doi: 10.1016/j.jbiotec.2011.03.006
  3. 3.
    Astrom K, Hagglund T (2006) Advanced PID control. ISA Instrumentation, Systems and Automation Society, Research Triangle ParkGoogle Scholar
  4. 4.
    Boudreau MA, McMillan GK (2007) New directions in bioprocess monitoring and control. ISA, Research Triangle ParkGoogle Scholar
  5. 5.
    Camacho EF, Bordons C (2004) Model predictive control in the process industry. Springer, LondonGoogle Scholar
  6. 6.
    Gnoth S, Jenzsch M, Simutis R, Lubbert A (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31:21–39CrossRefGoogle Scholar
  7. 7.
    Gnoth S, Simutis R, Lübbert A (2010) Selective expression of the soluble product fraction in Escherichia coli cultures employed in recombinant protein production processes. Appl Micriobiol Biotechnol 122:483–493Google Scholar
  8. 8.
    Jenzsch M, Simutis R, Lübbert A (2006) Generic model control of the specific growth rate in E. coli cultivations. J Biotechnol 122:483–493CrossRefGoogle Scholar
  9. 9.
    Jenzsch M, Gnoth S, Kleinschmidt M, Simutis R, Lübbert A (2007) Improving the batch-to-batch reproducibility of microbial cultures during recombinant protein production by regulation of the total carbon dioxide production. J Biotechnol 128:858–867CrossRefGoogle Scholar
  10. 10.
    Junker BH, Wang HY (2006) Bioprocess monitoring and computer control: key roots of the current PAT initiative. Biotechnol Bioeng 95:226–262CrossRefGoogle Scholar
  11. 11.
    Komives C, Parker RS (2003) Bioreactor state estimation and control. Curr Opin Biotechnol 14:468–474CrossRefGoogle Scholar
  12. 12.
    Kuprijanov A, Schaepe S, Sieblist C, Gnoth S, Simutis R, Lübbert A (2008) Variability control in fermentations—meeting the challenges raised by FDA’s PAT initiative. Bioforum Eur 12:38–41Google Scholar
  13. 13.
    Mandenius CF (2004) Recent developments in the monitoring, modelling and control of biological production systems. Bioprocess Biosyst Eng 26:347–351CrossRefGoogle Scholar
  14. 14.
    McMillan GK, Benton T, Zhang Y, Boudreau MA (2008) PAT tools for accelerated process development and improvement. Bioprocess Int 6:34–42Google Scholar
  15. 15.
    Nielsen J, Villadsen J (1994) Bioreaction engineering principles. Plenum, New YorkGoogle Scholar
  16. 16.
    Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11:733–746CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • A. Kuprijanov
    • 1
    • 2
  • S. Schaepe
    • 1
  • M. Aehle
    • 1
  • R. Simutis
    • 2
  • A. Lübbert
    • 1
  1. 1.Center for Bioprocess EngineeringMartin-Luther-University Halle WittenbergHalle/SaaleGermany
  2. 2.Institute of Automation and Control SystemsKaunas University of TechnologyKaunasLithuania

Personalised recommendations