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Soft sensors have been suggested as potent tools for on-line estimations of critical bioprocess variables to be able to control the biological process in an as high extent as possible. The formation of inhibitory by-products in the form of organic acids, caused by an overflow of glucose, is a problem in most bioprocesses expressing recombinant proteins.    In this project a new method of controlling the glucose feeding in an Escherichia coli fed-batch cultivation expressing the green fluorescent protein (GFP) was investigated. The new controller system implemented in the software controlled the feed rate based on on-line HPLC measurements of the concentration of organic acids.      The results showed that the controller managed to down-regulate the inhibitory organic acids to a low level as it tried to keep the glucose uptake rate at an optimum for maximum cell growth. The results suggested that the controller could be a powerful tool to create a more secure reproducibility and to generate high product yields in recombinant protein productions.

Increasing demand on more effective cell culture reactors has driven optimization works to increase output of products. This has led to development of soft sensors that uses mathematical formulas to increase the available information for the parameters during runs. In the project two parameters was evaluated for use in such a soft sensor, viability by measuring on-line capacitance with Aber probe and L-lactate production using BioSenz apparatus. To determine how well these could be used both were used on batch reactors measuring on a mouse-mouse B cell hybridoma culture which produced IgG1. On-line measurements were performed by probes which measured directly on the cell suspension or withdrew sterile sample from the reactor. Measuring viability gave results with low error, which can be concluded to the variation in reference cell count, but it could not be determined if measuring L-lactate production with BioSenz works in reactors of this size. More work needs to be done on other types of reactors, like fed-batch or perfusion, or lower working volumes.

Monoclonal antibodies (MAbs) have an expanding market for use in diagnostic and therapeutic applications. Industrial production of these biopharmaceuticals is usually achieved based on fed-batch cultures of mammalian cells in bioreactors (Chinese hamster ovary (CHO) and Hybridoma cells), which can express different kinds of recombinant proteins. In order to reach high cell densities in these bioreactors, it is necessary to carry out an optimization of their production processes. Hence, macroscopic model equations must be developed to describe cell growth, nutrient consumption and product generation. These models will be very useful for designing the bioprocess, for developing robust controllers and for optimizing its productivity.<p>This thesis presents a new kinetic model of hybridoma cell metabolism in fed batch culture and typical illustration of a systematic methodology for mathematical modelling, parameter estimation and model-based optimization and state estimation of bioprocesses. <p>In the first part, a macroscopic model that takes into account phenomena of overflow metabolism within glycolysis and glutaminolysis is proposed to simulate hybridoma HB-58 cell cultures. The model of central carbon metabolism is reduced to a set of macroscopic reactions. The macroscopic model describes three metabolism states: respiratory metabolism, overflow metabolism and critical metabolism. The model parameters and confidence intervals are obtained via a nonlinear least squares identification. It is validated with experimental data of fed-batch hybridoma cultures and successfully predicts the dynamics of cell growth and death, substrate consumption (glutamine and glucose) and metabolites production (lactate and ammonia). Based on a sensitivity analysis of the model outputs with respect to the parameters, a model reduction is proposed. <p>In the next step, the effort is directed to the maximization of biomass productivity in fed-batch cultures of hybridoma cells based on the overflow metabolism model. Optimal feeding rate, on the one hand, for a single feed stream containing both glucose and glutamine and, on the other hand, for two separate feed streams of glucose and glutamine are determined using a Nelder-Mead simplex optimization algorithm. Two different objective functions (performance criteria) are considered for optimization; the first criterion to be maximized is the biomass productivity obtained at the end of the fed-batch culture, the second criterion to be minimized is the difference between global substrate consumption and the maximum respiratory capacity.<p>The optimal multi exponential feed rate trajectory improves the biomass productivity by 10% as compared to the optimal single exponential feed rate. Moreover, this result is validated by the one obtained with the analytical approach in which glucose and glutamine are fed to the culture so as to control the hybridoma cells at the critical metabolism state, which allows maximizing the biomass productivity. The robustness analysis of optimal feeding profiles obtained with different optimization strategies is considered, first, with respect to parameter uncertainties and, finally, with respect to model structure errors.<p>Finally, the overflow metabolism model is used to develop an extended Kalman filter for online estimation of glucose and glutamine in hybridoma cell fed-batch cultures based on the considered available measurements (biomasses (on-line), lactate and ammonia (on-line or off-line)). The observability conditions are examined, and the performances are analysed with simulations of hybridoma cell fed-batch cultures. Glutamine estimation sensitivity is enforced by minimizing a cost function combining a usual least-squares criterion with a state estimation sensitivity criterion. <p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished