The large-scale production of monoclonal antibodies (MAb) by mammalian cells in batch and fed-batch culture systems is limited by the unwanted decline in cell viability and reduced productivity that may result from changes in culture conditions. Therefore, it becomes 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 to optimize monoclonal antibody (MAb) production in the future. Murine 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 metabolites from available extracellular concentration values. Based on the set of identified significant fluxes (from MFA), the original metabolic network was reduced to a 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 modes were compared to determine whether the same model structure could be applied to both the feeding profiles. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations in batch and fed-batch experiments. Quadratic Programming 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. It 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 operation 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. As a continuation of efforts to study the system in-depth, fluorescence imaging is currently being applied as a tool to capture the changes in cell morphology along the course of experimental batch and fed-batch runs. These experiments maybe able to elucidate the transition from non-apoptotic to apoptotic cells and this information maybe used in the future to improve the accuracy of the existing mathematical model.

The large-scale production of monoclonal antibodies (MAb) by mammalian cells in batch and fed-batch culture systems is limited by the unwanted decline in cell viability and reduced productivity that may result from changes in culture conditions. Therefore, it becomes 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 to optimize monoclonal antibody (MAb) production in the future. Murine 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 metabolites from available extracellular concentration values. Based on the set of identified significant fluxes (from MFA), the original metabolic network was reduced to a 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 modes were compared to determine whether the same model structure could be applied to both the feeding profiles. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations in batch and fed-batch experiments. Quadratic Programming 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. It 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 operation 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. As a continuation of efforts to study the system in-depth, fluorescence imaging is currently being applied as a tool to capture the changes in cell morphology along the course of experimental batch and fed-batch runs. These experiments maybe able to elucidate the transition from non-apoptotic to apoptotic cells and this information maybe used in the future to improve the accuracy of the existing mathematical model.

A 13C-Análise de Fluxos Metabólicos (13C-MFA) tornou-se uma técnica de alta precisão para estimar fluxos metabólicos e obter informações importantes sobre o metabolismo. Este método consiste em procedimentos experimentais, técnicas de medição e em cálculos para análise de dados. Neste contexto, os grupos de pesquisa de engenharia metabólica necessitam de ferramentas computacionais precisas e adequadas aos seus objetos de estudo. No presente trabalho, foi construída uma ferramenta computacional na plataforma MATLAB que executa cálculos de 13C-MFA, com balanços de metabólitos e cumômeros. Além disso, um módulo para estimar os fluxos metabólicos e um módulo para quantificar as incertezas das estimativas também foram implementados. O programa foi validado com dados presentes na literatura e aplicado a estudos de caso. Na estimação de fluxos de Pseudomonas sp. LFM046, identificou-se que esse micro-organismo possivelmente utiliza a Via das Pentoses em conjunto com a Via Entner-Doudoroff para a biossíntese de Polihidroxialcanoato (PHA). No design ótimo de experimentos para uma rede genérica de Pseudomonas, identificou-se a glicose marcada no átomo cinco como um substrato que permitirá determinar o fluxo na Via das Pentoses com menor incerteza. / 13C-Metabolic Flux Analysis (13C-MFA) has become a high-precision technique to estimate metabolic fluxes and get insights into metabolism. This method consists of experimental procedures, measurement techniques and data analysis calculations. In this context, metabolic engineering research groups demand accurate and suitable computational tools to perform the calculations. A computational tool was implemented in MATLAB platform that performs 13C-MFA calculation, using metabolite and cumomer balances, as well as a module to estimate the fluxes and a module to quantify their uncertainty. The program was validated with some classical cases from literature. From the flux estimates of Pseudomonas sp. LFM046, it was identified that the microorganism possibly uses the Pentose Phosphate Pathway along with the Entner-Doudoroff Pathway for Polyhydroxyalkanoate (PHA) biosynthesis. From the optimal experimental design for a generic Pseudomonas network, it was possible to conclude that glucose labeled at atom five is the best option to determine the flux in the Pentose Phosphate Pathway with smaller uncertainty.

This thesis is concerned with the derivation of bioprocess models intended for engineering purposes. In contrast with other techniques, the methodology used to derive a macroscopic model is based on available intracellular information. This information is extracted from the metabolic network describing the intracellular metabolism. The aspects of metabolic regulation are modeled by representing the metabolism of cultured cells with several metabolic networks. Here we present a systematic methodology for deriving macroscopic models when such metabolic networks are known. A separate model is derived for each “phase” of the culture. Each of these models relies upon a set of macroscopic bioreactions that resumes the information contained in the corresponding metabolic network. Such a set of macroscopic bioreactions is obtained by translating the set of Elementary Flux Modes which are well-known tools in the System Biology community. The Elementary Flux Modes are described in the theory of Convex Analysis. They represent pathways across metabolic networks. Once the set of Elementary Flux Modes is computed and translated into macroscopic bioreactions, a general model could be obtained for the type of culture under investigation. However, depending on the size and the complexity of the metabolic network, such a model could contain hundreds, and even thousands, of bioreactions. Since the reaction kinetics of such bioreactions are parametrized with at least one parameter that needs to be identified, the reduction of the general model to a more manageable size is desirable. Convex Analysis provides further results that allow for the selection of a macroscopic bioreaction subset. This selection is based on the data collected from the available experiments. The selected bioreactions then allow for the construction of a model for the experiments at hand.

Aquatic organisms in response to toxic insults from environmental pollutants activate defence systems including the aryl hydrocarbon receptor (AhR) in an attempt to metabolize and excrete these toxicants and their metabolites. These detoxification mechanisms however may come with certain energetic costs. I hypothesize that the activation of the AhR by β-Naphthoflavone (β-NF), a model AhR agonist, results in increased energetic costs requiring metabolic reorganization in rainbow trout hepatocytes. While the results obtained suggest that there are no significant energetic costs of AhR activation, analysis of enzyme activities suggests possible metabolic reorganization. This study also showed significant changes in cellular processes in hepatocytes over the incubation periods which previously were not reported. Furthermore, for the first time in fish hepatocytes, metabolic flux analysis (MFA) was used to examine intra-cellular metabolism, the applicability of which is discussed.

Aquatic organisms in response to toxic insults from environmental pollutants activate defence systems including the aryl hydrocarbon receptor (AhR) in an attempt to metabolize and excrete these toxicants and their metabolites. These detoxification mechanisms however may come with certain energetic costs. I hypothesize that the activation of the AhR by β-Naphthoflavone (β-NF), a model AhR agonist, results in increased energetic costs requiring metabolic reorganization in rainbow trout hepatocytes. While the results obtained suggest that there are no significant energetic costs of AhR activation, analysis of enzyme activities suggests possible metabolic reorganization. This study also showed significant changes in cellular processes in hepatocytes over the incubation periods which previously were not reported. Furthermore, for the first time in fish hepatocytes, metabolic flux analysis (MFA) was used to examine intra-cellular metabolism, the applicability of which is discussed.

Interleukin-3 is a cytokine, which acts on many target cells within the haemopoietic system, often in synergy with the other cytokines. Streptomyces lividans NCIMB 11416/IL3 p002 secreting human interleukin-3 was used as the host organism in this study of improving target protein production. Streptomyces also produces several proteases including extracellular endoprotease that truncate the N-terminus of the recombinant protein. Federal guidelines and regulations banning animal-derived medium components necessitate the refinement or redevelopment of industrial medium formulations. The development of a defined medium without animal products is most desirable for the production of pure and safe biological products. The objective of the proposed research was the development and application of engineering methodology for the development of a defined medium and the analysis and optimization of a bacterial bioprocess for recombinant protein production. The underlying hypothesis is that a significant improvement of target protein productivity is achievable by using appropriate optimization techniques. During the first phase of this study the task was to develop a systematic procedure for the design and optimization of a chemically defined medium. The study aimed at replacing casein peptone in conventional medium for S. lividans with essential amino acids and determining the optimum proportion of the amino acids. To accomplish this, starvation trials with growth limiting amino acids were performed to establish the baseline for the nutritional requirement. The starvation trials revealed that essential amino acids for growth and product formation are amongst the following eight amino acids: Arg, Asn, Asp, Glu, Leu, Met, Phe, and Thr. Following these preliminary experiments, a statistically based experimental method called mixture experiments along with distance-based multivariate analysis revealed that Asp, Leu, Met, and Phe were the essential amino acids. Then, another mixture experiment design known as simplex lattice design was performed and artificial neural networks were employed to obtain the optimum proportions of the essential amino acids. The optimal medium was found to be composed of 56% Asp, 5% Met, and 39% Phe. It was found in previous studies that in complex media, several types of protease are produced during fermentation. Using the defined medium no proteolytic activity was detected in the fermentation broth. The second optimization method was based on metabolic flux analysis. A comprehensive metabolic network was developed for S. lividans. The metabolic network included carbohyderate and amino acid metabolism in both anabolic and catabolic reactions. According to the experimental results, the time course of the fermentation was divided into two phases, Phase E1 and Phase E2. In the first phase amino acids were used as a nitrogen source and in the second phase ammonia was the nitrogen source for growth and product formation. The metabolic network was used to form a set of linear algebraic equations based on the stoichiometry of the reactions by assuming pseudo-steady state for intracellular metabolites. The metabolic flux model consisted of 62 intracellular metabolites and 91 biochemical reactions. Two different objective functions were considered for optimization: maximizing the specific growth rate and minimizing the redox equivalent. A linear programming approach was used for optimizing the objective functions. The proposed model was able to predict the specific growth rate very accurately with a maximum error of 10%. The oxygen uptake rate and carbon dioxide evolution rate were evaluated with maximum error of 27% and 35%, respectively. Sensitivity analysis revealed that amino acid uptake was the growth limiting flux during the Phase E1 of the fermentation. During Phase E2 the uptake rate of ammonia had a significant effect on the specific growth rate. Sensitivity analysis of the specific growth rate and redox potential with respect to the biomass components showed that any additional supply of biomass building blocks (amino acids, nucleotides) would not significantly affect the specific growth rate and redox potential production as well as the calculated flux pattern.

Interleukin-3 is a cytokine, which acts on many target cells within the haemopoietic system, often in synergy with the other cytokines. Streptomyces lividans NCIMB 11416/IL3 p002 secreting human interleukin-3 was used as the host organism in this study of improving target protein production. Streptomyces also produces several proteases including extracellular endoprotease that truncate the N-terminus of the recombinant protein. Federal guidelines and regulations banning animal-derived medium components necessitate the refinement or redevelopment of industrial medium formulations. The development of a defined medium without animal products is most desirable for the production of pure and safe biological products. The objective of the proposed research was the development and application of engineering methodology for the development of a defined medium and the analysis and optimization of a bacterial bioprocess for recombinant protein production. The underlying hypothesis is that a significant improvement of target protein productivity is achievable by using appropriate optimization techniques. During the first phase of this study the task was to develop a systematic procedure for the design and optimization of a chemically defined medium. The study aimed at replacing casein peptone in conventional medium for S. lividans with essential amino acids and determining the optimum proportion of the amino acids. To accomplish this, starvation trials with growth limiting amino acids were performed to establish the baseline for the nutritional requirement. The starvation trials revealed that essential amino acids for growth and product formation are amongst the following eight amino acids: Arg, Asn, Asp, Glu, Leu, Met, Phe, and Thr. Following these preliminary experiments, a statistically based experimental method called mixture experiments along with distance-based multivariate analysis revealed that Asp, Leu, Met, and Phe were the essential amino acids. Then, another mixture experiment design known as simplex lattice design was performed and artificial neural networks were employed to obtain the optimum proportions of the essential amino acids. The optimal medium was found to be composed of 56% Asp, 5% Met, and 39% Phe. It was found in previous studies that in complex media, several types of protease are produced during fermentation. Using the defined medium no proteolytic activity was detected in the fermentation broth. The second optimization method was based on metabolic flux analysis. A comprehensive metabolic network was developed for S. lividans. The metabolic network included carbohyderate and amino acid metabolism in both anabolic and catabolic reactions. According to the experimental results, the time course of the fermentation was divided into two phases, Phase E1 and Phase E2. In the first phase amino acids were used as a nitrogen source and in the second phase ammonia was the nitrogen source for growth and product formation. The metabolic network was used to form a set of linear algebraic equations based on the stoichiometry of the reactions by assuming pseudo-steady state for intracellular metabolites. The metabolic flux model consisted of 62 intracellular metabolites and 91 biochemical reactions. Two different objective functions were considered for optimization: maximizing the specific growth rate and minimizing the redox equivalent. A linear programming approach was used for optimizing the objective functions. The proposed model was able to predict the specific growth rate very accurately with a maximum error of 10%. The oxygen uptake rate and carbon dioxide evolution rate were evaluated with maximum error of 27% and 35%, respectively. Sensitivity analysis revealed that amino acid uptake was the growth limiting flux during the Phase E1 of the fermentation. During Phase E2 the uptake rate of ammonia had a significant effect on the specific growth rate. Sensitivity analysis of the specific growth rate and redox potential with respect to the biomass components showed that any additional supply of biomass building blocks (amino acids, nucleotides) would not significantly affect the specific growth rate and redox potential production as well as the calculated flux pattern.

Aquatic organisms in response to toxic insults from environmental pollutants activate defence systems including the aryl hydrocarbon receptor (AhR) in an attempt to metabolize and excrete these toxicants and their metabolites. These detoxification mechanisms however may come with certain energetic costs. I hypothesize that the activation of the AhR by β-Naphthoflavone (β-NF), a model AhR agonist, results in increased energetic costs requiring metabolic reorganization in rainbow trout hepatocytes. While the results obtained suggest that there are no significant energetic costs of AhR activation, analysis of enzyme activities suggests possible metabolic reorganization. This study also showed significant changes in cellular processes in hepatocytes over the incubation periods which previously were not reported. Furthermore, for the first time in fish hepatocytes, metabolic flux analysis (MFA) was used to examine intra-cellular metabolism, the applicability of which is discussed.

In this study, firstly, bioprocess characteristics for Serine Alkaline Protease (SAP) production, using recombinant Bacillus subtilis carrying pHV1431::subC, were examined. The cell concentration, substrate concentration, SAP activity and SAP synthesis rate profiles demonstrated that the system reaches to a steady state in terms of cell growth and SAP synthesis between t=15-25 h, therefore, this time interval is appropriate to employ both metabolic flux analysis and metabolic control analysis, which apply strictly to steady state systems. After that, three separate perturbations were introduced by addition of aspartate to the production medium at a certain time of the bioprocess. The response of the cells were observed and / by comparing the changes in intracellular reactions of aspartate pathway, Asn, Thr and Ile productions were determined to be the bottlenecks in aspartate pathway and the branchpoints splitting from Asp and AspSa were identified to be weakly rigid branchpoints. Lastly, metabolic control analysis principles were applied to determine the elasticity and flux control coefficients of the simplified aspartate pathway. Aspartate formation reaction and Lys, Thr, Ile, Met producing group share the control of asparagine synthesis. The results revealed that lysine producing branch flux dominates the other branch fluxes, therefore to eliminate bottlenecks and increase SAP production, the activity of the branches leading to the formation of Asn, Thr and Ile should be increased while decreasing the activity of lysine synthesizing branch. This could be achieved either by genetic manipulation or by addition of specific inhibitors or activators to the system.