Skip to main content
SpringerLink
Log in

Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach

  • Original Research
  • Published:
Cytotechnology Aims and scope Submit manuscript

Abstract

By means of a model predictive control strategy it was possible to ensure a high batch-to-batch reproducibility in animal cell (CHO-cell) suspensions cultured for a recombinant therapeutic protein (EPO) production. The general control objective was derived by identifying an optimal specific growth rate taking productivity, protein quality and process controllability into account. This goal was approached indirectly by controlling the oxygen mass consumed by the cells which is related to specific biomass growth rate and cell concentration profile by manipulating the glutamine feed rate. Process knowledge represented by a classical model was incorporated into the model predictive control algorithm. The controller was employed in several cultivation experiments. During these cultivations, the model parameters were adapted after each sampling event to cope with changes in the process’ dynamics. The ability to predict the state variables, particularly for the oxygen consumption, led to only moderate changes in the desired optimal operational trajectories. Hence, nearly identical oxygen consumption profiles, cell and protein titers as well as sialylation patterns were obtained for all cultivation runs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

IVC:

Integral of viable cells (109 cells h)

XV :

Viable cell density (109 cells L−1)

Xt :

Total cell density (109 cells L−1)

Glc:

Glucose concentration (mM)

Gln:

Glutamine concentration (mM)

Lac:

Lactate concentration (mM)

NH4 :

Ammonia concentration (mM)

P:

Protein (mg)

W:

Culture weight (kg)

F:

Overall feed rate (kg h−1)

FBase :

Base consumption rate (kg h−1)

FEvap :

Evaporation rate (kg h−1)

FGlc :

Glucose feed rate (kg h−1)

FGln :

Glutamine feed rate (kg h−1)

FSample :

Sampling rate (kg h−1)

XV0 :

Viable cell density at start of feeding (109 cells L−1)

W0 :

Culture weight at start of feeding (kg)

Sf :

Substrate feed concentration (mM)

S:

Substrate concentration (mM)

ρ:

Liquid density (kg L−1)

t:

Time (h)

t0 :

Start of feeding (h)

x:

Viable cells (109 cells)

μ:

Specific growth rate (h−1)

μnet :

Net specific growth rate μnet = μ − kd (h−1)

μset :

Setpoint specific growth rate (h−1)

qi :

Specific consumption/production rate of state variable i (mmol 109 cells−1 h−1)

qGlcmax :

Maximum specific glucose consumption rate (mmol 109 cells−1 h−1)

qGlnmax :

Maximum specific glutamine consumption rate (mmol 109 cells−1 h−1)

kd :

Cell decay rate (h−1)

kdmax :

Maximum cell decay rate (h−1)

kdeg :

Degradation constant for glutamine kdeg = 0.004 (h−1)

YSX :

Yield consumed Substrate per cells formed (mmol 109 cells−1 h−1)

YGlcX :

Yield glucose consumed per cells formed (mmol 109 cells−1)

YGlnX :

Yield glutamine consumed per cells formed (mmol 109 cells−1 h−1)

YLacGlc :

Yield lactate formed per glucose consumed (mmol mmol−1)

\( {\text{Y}}_{{{\text{NH}}_{4}{\rm X}}} \) :

Yield ammonia formed per cells formed (mmol 109 cells−1 h−1)

YOGlc :

Yield oxygen consumed per glucose consumed (mmol mmol−1)

YOGln :

Yield oxygen consumed per glutamine consumed (mmol mmol−1)

KGlc :

Glucose saturation constant (mM)

KGln :

Glutamine saturation constant (mM)

KLac :

Lactate inhibition constant for lactate (mM)

\( {\text{K}}_{{{\text{NH}}_{ 4}^{ + } }} \) :

Ammonia inhibition constant for ammonia (mM)

KO :

Inhibition constant (mM)

k1 :

Constant 1 for glucose (mM)

k2 :

Constant 2 for glucose (mM)

mGln :

Glutamine maintenance constant (mmol 109 cells−1 h−1)

mO :

Oxygen maintenance constant (mmol 109 cells−1 h−1)

\( {\text{n}}_{{{\text{O}}_{ 2} }} \) :

Amount of oxygen consumed (mmol)

mAA :

Specific ammonia production rate at low glutamine concentrations (mmol 109 cells−1 h−1) (Zeng et al. 1998)

OUR:

Oxygen uptake rate (mmol L−1 h−1)

tOUR:

Total oxygen uptake rate (mmol  h−1)

tcOUR:

\( ={\text{n}}_{{{\text{O}}_{ 2} }} \) total cumulative oxygen uptake rate (mmol)

References

  • Aehle M, Kuprijanov A, Schaepe S, Simutis R, Lübbert A (2011a) Increasing batch-to-batch reproducibility of CHO cultures by robust open-loop control. Cytotechnology 63:41–47

    Article  CAS  Google Scholar 

  • Aehle M, Schaepe S, Kuprijanov A, Simutis R, Lübbert A (2011b) Simple and efficient control of CHO cell cultures. J Biotechnol 153:56–61

    Article  CAS  Google Scholar 

  • Bork K, Reutter W, Weidemann W, Horstkorte R (2007) Enhanced sialylation of EPO by overexpression of UDP-GlcNAc 2-epimerase/ManAc kinase containing a sialuria mutation in CHO cells. FEBS Lett 581:4195–4198

    Article  CAS  Google Scholar 

  • Camacho EF, Bordons C (1995) Model predictive control in the process industry. Springer, London

    Book  Google Scholar 

  • Frahm B, Lane P, Atzert H, Munack A, Hoffmann M, Hass VC, Pörtner R (2002) Adaptive, model-based control by the open-loop-feedback-optimal (OLFO) controller for the effective fed-batch cultivation of hybridoma cells. Biotechnol Prog 18:1095–1103

    Article  CAS  Google Scholar 

  • Jenzsch M, Simutis R, Lübbert A (2004) Application of model predictive control to cultivation processes for protein production with genetically modified bacteria. In: Proceedings of the 9th international IFAC symposium, Nancy, pp 511–516

  • Pirt SJ (1975) Principles of microbe and cell cultivation. Blackwell Scientific Publications, Oxford

    Google Scholar 

  • Siegwart P, Côté J, Male K (1999) Adaptive control at low glucose concentration of HEK-293 cell serum-free cultures. Biotechnol Prog 15:608–616

    Article  CAS  Google Scholar 

  • Wang MD, Yang M, Huzel N, Butler M (2002) Erythropoietin production from CHO cells grown by continuous culture in a fluidized-bed reactor. Biotechnol Bioeng 77:194–203

    Article  CAS  Google Scholar 

  • Yüzgeç U, Palazoglu A, Romagnoli JA (2010) Refinery scheduling of crude oil unloading, storage and processing using a model predictive control strategy. Comput Chem Eng 34:1671–1686

    Article  Google Scholar 

  • Zeng A-P, Hu W-S, Deckwer W-D (1998) Variation of stoichiometric ratios and their correlation for monitoring and control of animal cell cultures. Biotechnol Prog 14:434–441

    Article  CAS  Google Scholar 

  • Zhang BJ, Hua B (2007) Effective MILP model for oil refinery-wide production planning and better energy utilization. J Clean Prod 15:439–448

    Article  Google Scholar 

Download references

Acknowledgments

The financial support by the Ministry of Science and Education by means of the Excellence Initiative Sachsen-Anhalt is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, Weinbergweg, 22, 06120, Halle (Saale), Germany

    Mathias Aehle, Sebastian Schaepe, Artur Kuprijanov & Andreas Lübbert

  2. Institute of Physiological Chemistry, Martin-Luther-University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany

    Kaya Bork & Rüdiger Horstkorte

  3. Institute of Automation and Control Systems, Kaunas University of Technology, Studentu g. 48, 3028, Kaunas, Lithuania

    Rimvydas Simutis

Authors
  1. Mathias Aehle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaya Bork
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sebastian Schaepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Artur Kuprijanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rüdiger Horstkorte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rimvydas Simutis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andreas Lübbert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Lübbert.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aehle, M., Bork, K., Schaepe, S. et al. Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach. Cytotechnology 64, 623–634 (2012). https://doi.org/10.1007/s10616-012-9438-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10616-012-9438-1

Keywords

Search

Navigation