Year: 2007
Type: dissertation
Abstract: The large-scale production of monoclonal antibodies (MAb) by mammalian cells in batch \nand fed-batch culture systems is limited by the unwanted decline in cell viability and \nreduced productivity that may result from changes in culture conditions. Therefore, it \nbecomes imperative to gain an in-depth knowledge of the factors affecting cell growth and cell viability that in turn determine the antibody production. An attempt has been made to obtain an overall model that predicts the behaviour of both batch and fed-batch systems as a function of the extra-cellular nutrient/metabolite concentrations. Such model formulation will aid in identifying and eventually controlling the dominant factors in play \nto optimize monoclonal antibody (MAb) production in the future. \n \nMurine hybridoma 130-8F producing anti-F-glycoprotein monoclonal antibody was grown in D-MEM medium (Gibco 12100) with 2% FBS. A systematic approach based on Metabolic Flux Analysis (MFA) was applied for the calculation of intracellular fluxes for \nmetabolites from available extracellular concentration values. Based on the set of \nidentified significant fluxes (from MFA), the original metabolic network was reduced to \na set of significant reactions. The reactions in the reduced metabolic network were then combined to yield a set of macro-reactions obeying Monod kinetics. Half saturation constants were fixed empirically to avoid computational difficulties that parameter estimation for an over-parameterized system of equations would cause. Using Quadratic Programming, the proposed Dynamic Model was calibrated and model prediction was carried out individually for batch and fed-batch runs. Flux distribution for batch and fedbatch \nmodes were compared to determine whether the same model structure could be applied to both the feeding profiles. Correlation analysis was performed to formulate a \nBiomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations in batch and fed-batch experiments. Quadratic \nProgramming was applied once again for estimation of growth and death coefficients in the equations for viable and dead cell predictions. The prediction accuracy of these model equations was tested by using experimental data from additional runs. Further, the Dynamic Model was integrated with the Biomass Model to get an Integrated Model capable of predicting concentration values for substrates, extracellular metabolites, and viable and dead cell concentration by utilizing only starting concentrations as input. \n \nIt was found that even though the set of significant fluxes was the same for batch and fedbatch operations, the order of these fluxes was different between the two systems. There was a gradual metabolic shift in the fed-batch system with time indicating that under conditions of nutrient limitation, the available energy is channeled towards maintenance rather than growth. Also, available literature with regard to cell kinetics during fed-batch \noperation suggests that under nutrient limited conditions, the cells move from a viable, non-apoptotic state to a viable apoptotic state. This is believed to lead to variations in antibody production rates and might explain inaccurate predictions for MAb obtained from the model proposed in the current work. As a result more detailed analysis of the system and in particular, the switch from non-apoptotic to apoptotic state is required. \n \nAs a continuation of efforts to study the system in-depth, fluorescence imaging is \ncurrently being applied as a tool to capture the changes in cell morphology along the \ncourse of experimental batch and fed-batch runs. These experiments maybe able to \nelucidate the transition from non-apoptotic to apoptotic cells and this information maybe \nused in the future to improve the accuracy of the existing mathematical model.
Source: UWSpace (University of Waterloo)
Author: Penny Dorka
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2607412786