430528 Simulation of Biopharmaceutical Bioreactor Using an Integrated CFD-Population Balance Modeling Approach

Thursday, November 12, 2015: 10:10 AM
Ballroom B (Salt Palace Convention Center)
Parham Farzan and Marianthi Ierapetritou, Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

Since mid-1980’s the specific productivity and density of cells in animal cell cultures have been increased several folds. These have been achieved via improving vectors, host cell engineering, selection, gene amplification and cell line screening [1] and optimum design of medium and feeding strategy and process engineering [2]. The necessity of integration of these goals has been established and partially addressed but a comprehensive bioreactor model in which extracellular and intracellular phenomena are coupled is still missing.

To represent the effect of agitation, aeration and feeding strategy on productivity, growth and viability of cells, the model of a large-scale bioreactor has multiple compartments which capture fluid dynamics, cell source variability, interphase mass transfer, gas-liquid interaction, and cell metabolism. The turbulent multiphase flow inside the bioreactor has been successfully modeled using k-e models of eddy viscosity provided in commercial CFD software packages [3]. The output of the CFD model is local velocity fields, shear and energy dissipation rates. The most important effect of shear on cells is lysis. To take into account the effect of this phenomenon on the viable cell density a population balance model can be implemented and integrated in which the death kernel includes lysis in addition to other causes of death. The merit of this type of modeling is in the prediction of the average behavior of a group of individuals showing randomness. Therefore it has been a trusted approach to model population of cells and to compute the distribution of cells over cellular states [4]. Due to low solubility of oxygen in water, high densities of cells quickly consume all of it in a saturated culture and produce enough carbon dioxide that it can have inhibitory effects. As a result addition and removal of gases are inevitable parts of operation of a large-scale fermenter. Dissolved oxygen concentration is calculated through estimation of gas-liquid mass transfer coefficients under the operating condition. The gas phase volume fraction can be calculated using gas hold-up correlations available in the literature or from the CFD with an additional Eulerian or discrete phase. The energy dissipated due to bubble rupture is usually higher than that in the bulk of the tank. So gas-liquid interaction directly affects the number of viable cells. Population balance modeling has the capability to capture the stochasticity involved in breakage and coalescence of bubbles and therefore is a good candidate for modeling the gas phase. Another advantage of this approach to capture the effects of bubbles is the more precise calculation of the gas-liquid interphase area; hence a better estimation of cell entrapment rate in the surface of bubbles. Besides lysis, bioreactor hydrodynamics also affects distribution and number of surface receptors, production of specific proteins, certain metabolic processes and can induce apoptosis [5]. Furthermore, nutrients and oxygen accessibilities to cells as well as the toxic effects of the material discharged from cells should also be taken into account. So a connection to cell metabolism seems necessary which can be built via a single cell model. Animal cells contain a large number of molecules so the main decision is the level of the complexity of the single cell model. Metabolic engineering and flux analysis can provide a framework to measure the importance of certain biochemical reactions and their effects on the systemic behavior of cells.

In summary, the model is composed of three main parts which represent bioreactor hydrodynamics, cells population and cell metabolism. The first component consists of a CFD coupled with a population balance model of bubbles. The result of solving this sub-model is nutrients and dissolved oxygen distributions, velocity fields and shear. The cells population model needs this information for calling the single cell model and calculation of its kernels; birth, death, growth and transition. The three components together connect protein production and viable cell density to operational parameters such as aeration and agitation rates and feeding schedule.


1.            Wurm, F.M., Production of recombinant protein therapeutics in cultivated mammalian cells. Nature Biotechnology, 2004. 22(11): p. 1393-1398.

2.            Xie, L.Z. and D.I.C. Wang, High cell density and high monoclonal antibody production through medium design and rational control in a bioreactor. Biotechnology and Bioengineering, 1996. 51(6): p. 725-729.

3.            Schmalzriedt, S., et al., Integration of physiology and fluid dynamics, in Process Integration in Biochemical Engineering. 2003. p. 19-68.

4.            Sidoli, F.R., S.P. Asprey, and A. Mantalaris, A Coupled Single Cell-Population-Balance Model for Mammalian Cell Cultures. Industrial and Engineering Chemistry Research, 2006. 45(16): p. 5801 - 5811.

5.            Shuler, M.L. and F. Kargi, Bioprocess Engineering. 2 ed. 2002: Prentice Hall.

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