Skip to main content
Log in

Verhulst and stochastic models for comparing mechanisms of MAb productivity in six CHO cell lines

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

Abstract

The present study validates previously published methodologies—stochastic and Verhulst—for modelling the growth and MAb productivity of six CHO cell lines grown in batch cultures. Cytometric and biochemical data were used to model growth and productivity. The stochastic explanatory models were developed to improve our understanding of the underlying mechanisms of growth and productivity, whereas the Verhulst mechanistic models were developed for their predictability. The parameters of the two sets of models were compared for their biological significance. The stochastic models, based on the cytometric data, indicated that the productivity mechanism is cell specific. However, as shown before, the modelling results indicated that G2 + ER indicate high productivity, while G1 + ER indicate low productivity, where G1 and G2 are the cell cycle phases and ER is Endoplasmic Reticulum. In all cell lines, growth proved to be inversely proportional to the cumulative G1 time (CG1T) for the G1 phase, whereas productivity was directly proportional to ER. Verhulst’s rule, “the lower the intrinsic growth factor (r), the higher the growth (K),” did not hold for growth across all cell lines but held good for the cell lines with the same growth mechanism—i.e., r is cell specific. However, the Verhulst productivity rule, that productivity is inversely proportional to the intrinsic productivity factor (r x ), held well across all cell lines in spite of differences in their mechanisms for productivity—that is, r x is not cell specific. The productivity profile, as described by Verhulst’s logistic model, is very similar to the Michaelis–Menten enzyme kinetic equation, suggesting that productivity is more likely enzymatic in nature. Comparison of the stochastic and Verhulst models indicated that CG1T in the cytometric data has the same significance as r, the intrinsic growth factor in the Verhulst models. The stochastic explanatory and the Verhulst logistic models can explain the differences in the productivity of the six clones.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Agrawal P, Koshy G, Ramseier M (1989) An algorithm for operating a fed-batch fermentor at optimum specific growth rate. Biotechnol Bioeng 33:115–125

    Article  CAS  Google Scholar 

  • Al-Rubeai M, Emery AN (1990) Mechanisms and kinetics of monoclonal antibody synthesis and secretion in synchronous and asynchronous hybridoma cell cultures. J Biotechnol 16:67–85

    Article  CAS  Google Scholar 

  • Al-Rubeai M, Singh RP (1998) Apoptosis in cell culture. Curr Opin Biotechnol 9:152–156

    Article  CAS  Google Scholar 

  • Al-Rubeai M, Emery AN, Chalder S, Jan DC (1992) Specific monoclonal antibodyproductivity and the cell cycle comparisons of batch, continuous and perfusion cultures. Cytotechnology 9:85–97

    Article  CAS  Google Scholar 

  • Arakaki NT, Nishihama H, Owaki YK, Suenaga M, Miyoshi E, Emoto Y, Shibata H, Shono M, Higuti T (2006) Dynamics of mitochondria during the cell cycle. Biol Pharm Bull 29:1962–1965

    Article  CAS  Google Scholar 

  • Bi Jing-Xiu, Shuttleworth J, Al-Rubeai M (2004) Uncoupling of cell growth and proliferation results in enhancement of productivity. Biotechnol Bioeng 85:741–749

    Article  CAS  Google Scholar 

  • Butler M (2005) Animal cell cultures: recent achievements and perspectives in the production of biopharmaceuticals. Appl Microbiol Biotechnol 68:283–291

    Article  CAS  Google Scholar 

  • Carroll S, Naciri M, Al-Rubeai M (2007) Monitoring of growth, physiology and productivity of animal cells by flow cytometry. In: Portner R (ed) Animal cell biotechnology: methods and protocols. Methods in biotechnology, vol 24. Humana Press, pp 223–238

  • Chatterjee S, Price B (1991) Regression analysis by example. Wiley, Hoboken, NJ

    Google Scholar 

  • Cooper S (2001) Revisiting the relationship of the mammalian G1 phase to cell differentiation. J Theor Biol 208:399–402

    Article  CAS  Google Scholar 

  • Duncan A (2002) Antibodies hold the key. Chem Ind 5:14–16

    Google Scholar 

  • Dutton RL (1998) Growth and productivity of a recombinant chines hamster ovary cell line in batch culture. PhD Thesis

  • Frame KK, Hu W-S (1991) Kinetic study of hybridoma cell growth in continuous culture ii. behavior of producers and comparison to nonproducers. Biotech Bioeng 38:1020–1028

    Article  CAS  Google Scholar 

  • Frykman S, Srienc F (2001) Cell-cycle dependent protein secretion by saccharomyces cerevisiae. Biotechnol Bioeng 76:259–268

    Article  CAS  Google Scholar 

  • Fussengger M, Morris RP, Fux C, von Stockar B, Thompson CJ, Bailey JE (2000) Streptogramin-based gene regulation systems for mammalian cells Nat. Biotechnol 18:1203–1208

    Google Scholar 

  • Gold HJ (1977) Mathematical modelling of biological systems. An Introductory Guidebook Wiley, New York

    Google Scholar 

  • Goudar CT, Joeris K, Konstantinov KB, Piret JM (2005) Logistic equations effectively model mammalian cell batch and fed-batch kinetics by logically constraining the fit. Biotechnol Prog 21:1109–1118

    Article  CAS  Google Scholar 

  • Hayter PM (1989) An investigation into factors that affect monoclonal antibody production by hybridomas in culture. PhD Thesis submitted to Surrey University, Gullford Surrey, UK

  • Henderson MH, Ting-Beall HP, Tran-Sun-Tay R (1992) Shear sensitivity of mitotic doublets in GAP A3 hybridoma cells ASME. Bioprocess Eng Symp BED 23:7

    Google Scholar 

  • Hua Z, Graham TR (2009) The Golgi apparatus. In: Segev N (ed) Trafficking inside cells: pathways, mechanisms and regulation. Springer, Austin

    Google Scholar 

  • Ibarra N, Watanabe S, Bi J-X, Shuttleworth J, Al-Rubeai M (2003) Modulation of cell cycle for enhancement of antibody productivity in perfusion culture of NS0 cells. Biotech Prog 19:224–228

    Article  CAS  Google Scholar 

  • Jorgensen P, Tyers M (2004) How cells coordinate growth and division. Curr Biol 14:R1014–R1027

    Article  CAS  Google Scholar 

  • Kaufmann H, Mazur X, Marone R, Bailey JE, Fussenegger M (2001) Comparative analysis of two controlled proliferation strategies regarding product quality, influence on tetracycline-regulated gene expression, and productivity. Biotechnol Bioeng 72:592–602

    Article  CAS  Google Scholar 

  • KET Industrial Biotechnology (2011) Working group report. Sherpa Group and other stakeholders

  • Kleizen B, Braakman I (2004) Protein folding and quality control in the endoplasmic reticulum. Curr Opin Cell Biol 16:343–349

    Article  CAS  Google Scholar 

  • Korke Rashmi Gatti, de Leon Marcela, Yin Lau Ally, Lei Lim, Seow Justin Wee Eng, Teck Keong, Chung Maxey Ching Ming, WeiShou Hu (2004) Large scale gene expression profiling of metabolic shift of mammalian cells in culture. J Biotecnol 107:1–17

    Article  CAS  Google Scholar 

  • Krebs CJ (1996) Ecology, 4th edn. Harper and Row Publishers, New York, pp 198–229

    Google Scholar 

  • Kumar N, Gammell P, Clynes M (2007) Proliferation control strategies to improve productivity and survival during CHO based production culture. Cytotechnology 53:33–46

    Article  CAS  Google Scholar 

  • Lai T, Yang Y, Ng SK (2013) Advances in mammalian cell line development technologies for recombinant protein production. Pharmaceuticals 6:579–603

    Article  CAS  Google Scholar 

  • Lee F, Vijayasankaran N, Shen A, Kiss R, Amanullah A (2007) Cell culture processes for monoclonal antibody production. mAbs 2:466–477

    Google Scholar 

  • Li F, Vijayasankaran N, Shen A, Kiss R, Amanullah A (2010) Cell selection processes for monoclonal antibody production. mAbs 2:466–477

    Article  Google Scholar 

  • Linardos TI, Kalogerakis N, Behie LA (1991) The effect of specific growth rate and death rate on monoclonal antibody production in hybridoma chemostat culture. Can J Chem Engin 69:429–438

    Article  CAS  Google Scholar 

  • Malhotra JD, Kaufman RJ (2011) ER stress and its functional link to mitochondria: role in cell survival and death. Cold Spring Harb Perspect Biol 3(9). doi:10.1101/cshperspect.a004424

  • Miller WM, Blanch LW, Wilke CR (1986a) Kinetic analysis of hybridoma growth in continuous suspension culture. Presented at the ACS National Meeting, Anaheim, USA

  • Miller WM, Blanch LW, Wilke CR (1986b) Kinetic analysis of hybridoma growth and metabolism in batch and continuous suspension culture: effect of nutrient concentration, dilution rate and pH. Biotechnol Bioeng 32:947–965

    Article  Google Scholar 

  • Mitchison JM (1971) The biology of the cell cycle. Cambridge University Press, Cambridge

    Google Scholar 

  • Mitra K, Wunder C, Roysam B, Lin G, Lippincott-Schwartz J (2009) A hyperfused mitochondrial state achieved at G1–S regulates cyclin E buildup and entry into S phase. Proc Natl Acad Sci USA 106:11960–11965

    Article  CAS  Google Scholar 

  • Müller S, Lasche A (2004) Population profile of commercial yeast strain in the course of brewing. J Food Eng 63:375–381

    Article  Google Scholar 

  • Needham D, Ting-Beall HP, Tran-Son-Tay R (1990) Morphology and mechanical properties of GAP A3 hybridoma cells as related to cell cycle. ASME Bioprocess Eng Symp BED 16:5–10

    Google Scholar 

  • Plemper RK, Wolf DH (1999) Endoplasmic reticulum degradation reverse protein transport and its end in the proteasome. Mol Biol Rep 26:125–130

    Article  CAS  Google Scholar 

  • Ramirez OT, Mutharasan R (1990) Cell cycle and growth phase dependent variations in the distribution, antibody productivity and oxygen demand in hybridoma cultures. Biotechnol Bioeng 36:839–848

    Article  CAS  Google Scholar 

  • Rapoport TA, Goder V, Heinrich SU, Matlack KES (2004) Membrane-protein integration and the role of translocation. Trends Cell Biol 14:568–575

    Article  CAS  Google Scholar 

  • Shirsat N, Mohd A, English NJ, Glennon B, Al-Rubeai M (2013) Application of statistical techniques for elucidating flow cytometric data of batch and fed-batch cultures. Biotechnol Appl Biochem 60:536–545

    Article  CAS  Google Scholar 

  • Shirsat N, Mohd A, English NJ, Glennon B, Al-Rubeai M (2014) Cytotechnology (in press)

  • Sidoli FR, Mantalaris A, Asprey SP (2004) Modelling of mammalian cells and cell culture processes. Cytotechnology 44:27–46

    Article  CAS  Google Scholar 

  • Thomas JN (1990) Mammalian cell physiology. In: Lubinieck AS (ed) Large scale mammalian cell culture technology. Marcel Dekker INC, New York, p 94

    Google Scholar 

  • Trummer E, Fauland K, Seidinger S, Schriebl K, Lattenmayer C, Kunert R, Vorauer-Uhl K, Weik R, Borth N, Katinger H, Muller D (2006) Process parameter shifting: part II. Biphasic cultivation—a tool for enhancing the volumetric productivity of batch processes using Epo-Fc expressing CHO cells. Biotechnol Bioeng 94:1045–1052

    Article  CAS  Google Scholar 

  • Uchiyama K, Shioya S (1999) Modeling and optimization of α-amylase production. J Biotechnol 71:133–141

    Article  CAS  Google Scholar 

  • Uchiyama K, Morimoto M, Yokoyama Y, Shioya S (1997) Cell cycle dependency of rice α-amylase production in a recombinant yeast. Biotechnol Bioeng 54:262–271

    Article  CAS  Google Scholar 

  • Wang G, Seeman J (2011) Golgi biogenesis. Cold Spring Harb Prospect Biol 3:1–13

    Google Scholar 

  • Whelan, Keogh (2012) Controlling the living factory: modelling a mammalian cell culture for online process control system, ABB review 1/12. ABB Technology Ltd., Zurich, pp 40–46

  • Xie L, Wang DIC (1995) Application of improved stoichiometric model in medium design and fed-batch cultivation of animal cells in bioreactor. Cytotechnology 15:17–29

    Article  Google Scholar 

  • Zeng AO, Deckwer WD (1999) Model simulation and analysis of perfusion culture of mammalian cells at high cell density. Biotechnol Prog 15:373–382

    Article  CAS  Google Scholar 

  • Zeng AO, Deckwer WD, Hu WS (1998) Determination and rate laws of growth and death of hybridoma cells in continuous. Biotechnol Bioeng 57:642–654

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Al-Rubeai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirsat, N., Avesh, M., English, N.J. et al. Verhulst and stochastic models for comparing mechanisms of MAb productivity in six CHO cell lines. Cytotechnology 68, 1499–1511 (2016). https://doi.org/10.1007/s10616-015-9910-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10616-015-9910-9

Keywords

Navigation