Macroscopic models of Chinese hamster ovary cell cultures

Sammanfattning: Biopharmaceuticals treat a range of diseases, and is a growing sector within the pharmaceutical industry. A majority of these complex molecules are produced by genetically modified mammalian cells in large-scale cell cultures. Biopharmaceutical process development is costly and labor intensive, and has often been based on time-consuming empirical methods and trial-and-error. Mathematical modeling has great potential to speed up this work. A central question however, engaging researchers from various fields, is how to translate these complex biological systems into feasible and useful models.For biopharmaceutical production, macroscopic kinetic flux modeling has been proposed. This model type is derived from typical data obtained in the industry, and has been able to simulate cell growth and the uptake/secretion of important metabolites. Often, however, their scope is limited to specific culture conditions due to e.g. the lack of information on reaction kinetics, limited data sets, and simplifications to achieve calculability.In this thesis, the macroscopic kinetic model type is the starting point, but the goal is to capture a variety of culture conditions, as will be necessary for future applications in process optimization. The effects of varied availability of amino acids in the culture medium on cell growth, uptake/secretion of metabolites, and product secretion were studied in cell cultures.In Paper I, the established methodology of Metatool was tested: (i) a simplified metabolic network of approx. 30 reactions was defined; (ii) all possible so-called elementary flux modes (EFMs) through the network were identified using an established mathematical algorithm; and (iii) the effect on each flux was modelled by a simplified generalized kinetic equation. A limitation was identified; the Metatool algorithm could only handle simple networks, and therefore several reactions had to be discarded. In this paper, a new strategy for the kinetics was developed. A pool of alternative kinetic equations was created, from which a smaller set could be given higher weight as determined via data-fitting. This improved the simulations.The identification of EFMs was further studied in papers II–IV. In Paper II, a new algorithm was developed based on the column generation optimization technique, that in addition to the network also accounts for the data from one of the parallel cultures. The method identifies a subset of the EFMs that can optimally fit the data, even in more complex metabolic networks.In Paper III, a kinetic model based on EFM subsets in a 100 reaction network was generated, which further improved the simulations. Finally, in Paper IV, the algorithm was extended to EFM identification in a genome-scale network. Despite the high complexity, small subsets of EFMs relevant to the experimental data could be e ciently identified.