Modeling Of Myeloma Cell Cultivations

Nowadays, the recombinant microbial and mammalian cells are used to produce genetic engineering pharmaceuticals, such as monoclonal antibodies, growth factors and hormones. Mammalian cells are characterized by the ability to produce high-value biopharmaceutical products that require post-translation modification in order to become biologically active. These high-value products are produced in relatively small quantities due to the highly specialized culture conditions and their susceptibility to the reduced productivity or cell death as a result of slight deviations in the culture conditions. Models of cell processes are particularly useful for simulating, optimizing and controlling the cell culture processes, and eventually contribute to efforts to increase productivity. In this PhD thesis, three models with different purposes are proposed and validated with myeloma cell cultivations. These three models will be demonstrated as follows:Firstly, we developed an improved macrokinetic model with lumped parameters for myeloma cell cultivations.The traditional macrokinetic models with lumped parameters, especially the Monod model, are simple and therefore, are easy to be developed. They are widely used for applications. However, Monod-type kinetics is not able to describe the lag phase after inoculation or after pulse feeding. In order to deal with the this growth lag phase, a model based on the metabolic regulation mechanisms is developed. This metabolic regulation model together with the Monod-type model is better to describe the specific growth rate. In addition, the amino acid limitations are considered in modeling to account for the short-term period of cell growth after amino acid depletion.The improved macrokinetic model is validated with batch culture and fed-batch culture with pulse feedings. It shows that the model is able to simulate the concentrations of glucose, glutamine, lysine and lactate, as well as the densities of total, viable and dead cell with a suitable accuracy.Secondly, we developed a population balance model based on cell cycle mechanisms for myeloma cell cultivation. Mammalian cell cultures comprise heterogeneous cells that differ according to their size and intracellular levels of DNA, protein and other cell properties. Cell population distributions with respect to different cell properties are important for the control of the cell-cycle specific products. Population balance model is the most rigorous approach for describing the dynamics of the distributions of different cell properties. However, futher studies are still needed to improve this model by considering the cell-cycle specific properties. Moreover, few population balance models developed in the literature have been validated by experiments. Therefore, a population balance model here is developed based on cell-cycle mechanisms for myeloma cell cultivations. In this model, both cell volume and DNA content are used to differentiate individual cells in the cell population. Thus, the model can be used to simulate the distributions of cell volume and DNA content. The model simulations of DNA distribution are validated with the DNA measurements from flowcytometer with a reasonable accuracy. The dynamics can also be described by this model in terms of the cell fraction of each phase, concentrations of glucose, glutamine, lysine, ammonia, lactate and alanine, as well as the cell densities of total, viable and dead cells. These are also validated by experimental data.Compared with the macrokinetic model with lumped parameters, population balance model can provide more information, such as the dynamics of DNA distribution and phase fractions, for the control of the cell cycle dynamics. Furthermore, the DNA simulation results are helpful for the design of cell-cycle controlling process. Nevertheless, there is a challenge that population balance models may easily suffer from computational intensity.Thirdly, we developed a cybernetic model based on the regulation of enzyme systems for myeloma cell cultivations.Cybernetic models are structured on the basis of the regulation of the metabolic network and the cybernetic principles. Metabolic network is regulated by enzyme synthesis and enzyme activity that are represented by the cybernetic variabes'u'and'v', respectively. Enzyme systems are hypothesized to compete for optimizing resource utilization, and different pathways can be up and down regulated depending on the outcome of these competitions. The competition between deamination pathway and transamination pathway to use glutamine to produceα-KG is considered in this study. This is because these two pathways lead to different level of glutamine utilization and different quantity of byproducts (i.e. ammonia and alanine). In addition, the competition between lysine utilization for protein formation and energy supply is also involved due to lysine limitation observed in our experiments. In addition to the densities of viable and dead cells, concentrations of substrates, the byproducts, for example ammonia, lactate and alanine are simulated by this model. Model simulations also provide the levels of enzyme and other intracellular species, which are useful for the control of the mammalian cell cultures. Cybernetic models are powerful for capturing the dynamic response to environmental changes because they take into account the metabolic regulation of the network.It can be concluded that these three models will be selected for different applications according to their features. The macrokinetic models with lumped parameters have simple mathematical formulations. They are the most suitable alternative if the cell culture processes with slight perturbation and uncertainty are in question. Population balance models are dominant in the applications with the control of cell-cycle specific products involved. Contrary to the macrokinetic model with lumped parameters, cybernetic models show advantages when the cell culture processes with strong perturbation and uncertainty are considered.Finally, the models are used for further study of the simulation, optimization and control of the bioprocesses.