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. 1999 May-Jun;15(3):373-82.
doi: 10.1021/bp990040a.

Model simulation and analysis of perfusion culture of mammalian cells at high cell density

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Model simulation and analysis of perfusion culture of mammalian cells at high cell density

A P Zeng et al. Biotechnol Prog. 1999 May-Jun.

Abstract

Rate equations recently proposed by the authors for growth, death, consumption of nutrients, and formation of lactic acid, ammonium, and monoclonal antibody of hybridoma cells are used to simulate and analyze the behavior of perfusion cultures. Model simulations are in good agreement with experimental results from three different cell lines under varied perfusion and cell bleed rates except for cultures with very low viability. Analysis of simulations and experimental results indicates that in perfusion cultures with a complete cell separation cell bleed rate is a key parameter that strongly affects all the process variables, whereas the perfusion rate mainly affects the total and viable cell concentrations and the volumetric productivity of monoclonal antibody. Growth rate, viability, and specific perfusion rate of cells are only a function of the cell bleed rate. This also applies to cultures with partial cell separation in the permeate if the effective cell bleed rate is considered. It is suggested that the (effective) cell bleed rate of a perfusion culture should be carefully chosen and controlled separately from the perfusion rate. In general, a low cell bleed rate that warrants a reasonable cell viability appears to be desirable for the production of antibodies. Furthermore, model simulations indicate the existence of an optimum initial glucose concentration in the feed. For the cell lines considered, the initial glucose concentration used in normal cell culture media is obviously too high. The initial glutamine concentration can also be reduced to a certain extent without significantly impairing the growth and antibody production but considerably reducing the ammonia concentration. The mathematical model can be used to predict these optimum conditions and may also be used for process design.

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