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Widespread sedentary lifestyle and unhealthy eating habits in the last few decades have resulted in a dramatic increase of the number of people affected by obesity, type 2 diabetes, and cardiovascular diseases. The study of these pathological conditions revealed that impaired metabolism often causes these disorders. Lipid metabolism research has contributed significantly to determining mechanisms underlying metabolic disorders. Omega-3 fatty acids are an interesting target for lipidomics studies because they were shown to lower risk of cardiovascular diseases and are hypothesized to regulate lipid metabolism. In this work, I optimized lipid extraction and chemical modification methods for analysis of fatty acids profile of tissue samples and biofluids using comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GCxGC-MS). At first, I evaluated the relative amount of omega-3 fatty acids in red blood cells (Omega-3 index) of people living in Czech Republic in either the capital city (n=476) or the rural region (n=388). For this large-scale project, I extracted phospholipids from red blood cell (RBC) membranes, transesterified them into fatty acid methyl esters (FAMEs), and measured their profile by GCxGC-MS. The mean Omega-3 index was 3.56 mol % and I detected no significant...

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.

Recombinant protein production by mammalian cells is a core component of today’s multi-billion dollar biopharmaceutical industry. Transcriptome and proteome technologies have been used to probe for cellular components that correlate with higher cell-specific productivity, but have yet to yield results that can be translated into practical metabolic engineering strategies. The recognition of cellular complexity has led to an increasing adoption of systems biology, a holistic investigation approach that aims to bring together different omics technologies and to analyze the resulting datasets under a unifying context. Fluxomics is chosen as the platform context to investigate cell metabolism because it captures the integrated effects of gene expression, enzyme activity, metabolite availability and regulation, thereby providing a global picture of the cell’s metabolic phenotype. At present, the routine quantification of cell metabolism revolves around very basic cellular parameters: growth, substrate utilization and product formation. For a systems approach, however, just measuring gross metabolic features is insufficient; we are compelled to perform high-resolution, large-scale fluxomics in order to match the scale of other omics datasets. The challenges of performing large-scale fluxomics come from two opposing fronts. Metabolic flux analysis (MFA) is the estimation of intracellular fluxes from experimental data using a stoichiometric model, a process very much susceptible to modelling biases. The in silico challenge is to construct the most comprehensive model to represent the metabolism of a specific cell, while the in vivo challenge is to resolve as many fluxes as possible using experimental measurements or constraints. A compromise needs to be established between maximizing the resolution of the MFA model and working within technical limitations of the flux experiment. Conventional MFA models assembled from textbook pathways have been available for animal cell culture for the past 15 years. A state-of-the-art model was developed and used to analyse continuous hybridoma culture and batch CHO cell culture data (Chapter 3). Reasonable metabolic assumptions combined with constraint based analysis exploiting irreversibility constraints enabled the resolution of most fluxes in central carbon metabolism. However, while the results appear consistent, there is insufficient information in conventional measurement of uptake, secretion and growth data to assess the completeness of the model and validity of all assumptions. 13C metabolic flux analysis (13C MFA) can potentially resolve fluxes in the central carbon metabolism using flux constraints generated from 13C enrichment patterns of metabolites, but the multitude of substrate uptakes (glucose and amino acids) seen in mammalian cells, in addition to the lack of 13C enrichment data from proteinogenic amino acids, makes it very difficult to anticipate how a labelling experiment should be carried out. The challenges above have led to the development of a systematic workflow to perform large-scale MFA for mammalian cells. A genome-scale model (GeMs), an accurate compilation of gene-protein-reaction-metabolite associations, is the starting basis to perform whole-cell fluxomics. A semi-automated method was developed in order to rapidly extract a prototype of GeM from KEGG and UniProtKB databases (Chapter 4). Core metabolic pathways in the mouse GeM are mostly complete, suggesting that these databases are comprehensive and sufficient. The rapid prototyping system takes advantage of this, making long term maintenance of an accurate and up-to-date GeM by an individual possible. A large number of under-determined pathways in the mouse GeM cannot be resolved by 13C MFA because they do not produce any distinctive 13C enrichment patterns among the carbon metabolites. This has led to the development of SLIPs (short linearly independent pathways) for visualizing these under-determined metabolic pathways contained in large-scale GeMs (Chapter 5). Certain SLIPs are subsequently removed based on careful consideration of their pathway functions and the implications of their removal. A majority of SLIPs have a cyclic configuration, sharing similar redox or energy co-metabolites; very few represent true conversion of substrates to products. Of the 266 under-determined SLIPs generated from the mouse GeM, only 27 SLIPs were incorporated into the final working model under the criterion that they are significant pathways and are potentially resolvable by tracer experiments. Most of these SLIPs are degradation pathways of essential amino acids and inter-conversion of non-essential amino acids (Chapter 8). In parallel, OpenFLUX was developed to perform large-scale isotopic 13C MFA (Chapter 6). This software was built to accept multiple labelled substrates, and no restriction has been placed on the model type or enrichment data. These are necessary features to support large-scale flux analysis for mammalian cells. This was followed by the development of a design strategy that uses analytical gradients of isotopomer measurements to predict resolvability of free fluxes, from which the effectiveness of various 13C experimental scenarios using different combinations of input substrates and isotopomer measurements can be evaluated (Chapter 7). Hypothetical and experimental results have confirmed the predictions that, when glucose and glutamate/glutamine are simultaneously consumed, two separate experiments using [U-13C]- and [1-13C]-glucose, respectively, should be performed. If there is a restriction to a single experiment, then the 80:20 mixture of [U-13C]- and [1-13C]-glucose can provide a better resolution than other labelled glucose mixtures (Chapter 7 and Chapter 8). The tools and framework developed in this thesis brings us within reach of performing large-scale, high-resolution fluxomics for animal cells and hence realising systems-level investigation of mammalian metabolism. Moreover, with the establishment of a more rigorous, systematic modelling approach and higher functioning computational tools, we are now at a position to validate mammalian cell culture flux experiments performed 15 years ago.

Recombinant protein production by mammalian cells is a core component of today’s multi-billion dollar biopharmaceutical industry. Transcriptome and proteome technologies have been used to probe for cellular components that correlate with higher cell-specific productivity, but have yet to yield results that can be translated into practical metabolic engineering strategies. The recognition of cellular complexity has led to an increasing adoption of systems biology, a holistic investigation approach that aims to bring together different omics technologies and to analyze the resulting datasets under a unifying context. Fluxomics is chosen as the platform context to investigate cell metabolism because it captures the integrated effects of gene expression, enzyme activity, metabolite availability and regulation, thereby providing a global picture of the cell’s metabolic phenotype. At present, the routine quantification of cell metabolism revolves around very basic cellular parameters: growth, substrate utilization and product formation. For a systems approach, however, just measuring gross metabolic features is insufficient; we are compelled to perform high-resolution, large-scale fluxomics in order to match the scale of other omics datasets. The challenges of performing large-scale fluxomics come from two opposing fronts. Metabolic flux analysis (MFA) is the estimation of intracellular fluxes from experimental data using a stoichiometric model, a process very much susceptible to modelling biases. The in silico challenge is to construct the most comprehensive model to represent the metabolism of a specific cell, while the in vivo challenge is to resolve as many fluxes as possible using experimental measurements or constraints. A compromise needs to be established between maximizing the resolution of the MFA model and working within technical limitations of the flux experiment. Conventional MFA models assembled from textbook pathways have been available for animal cell culture for the past 15 years. A state-of-the-art model was developed and used to analyse continuous hybridoma culture and batch CHO cell culture data (Chapter 3). Reasonable metabolic assumptions combined with constraint based analysis exploiting irreversibility constraints enabled the resolution of most fluxes in central carbon metabolism. However, while the results appear consistent, there is insufficient information in conventional measurement of uptake, secretion and growth data to assess the completeness of the model and validity of all assumptions. 13C metabolic flux analysis (13C MFA) can potentially resolve fluxes in the central carbon metabolism using flux constraints generated from 13C enrichment patterns of metabolites, but the multitude of substrate uptakes (glucose and amino acids) seen in mammalian cells, in addition to the lack of 13C enrichment data from proteinogenic amino acids, makes it very difficult to anticipate how a labelling experiment should be carried out. The challenges above have led to the development of a systematic workflow to perform large-scale MFA for mammalian cells. A genome-scale model (GeMs), an accurate compilation of gene-protein-reaction-metabolite associations, is the starting basis to perform whole-cell fluxomics. A semi-automated method was developed in order to rapidly extract a prototype of GeM from KEGG and UniProtKB databases (Chapter 4). Core metabolic pathways in the mouse GeM are mostly complete, suggesting that these databases are comprehensive and sufficient. The rapid prototyping system takes advantage of this, making long term maintenance of an accurate and up-to-date GeM by an individual possible. A large number of under-determined pathways in the mouse GeM cannot be resolved by 13C MFA because they do not produce any distinctive 13C enrichment patterns among the carbon metabolites. This has led to the development of SLIPs (short linearly independent pathways) for visualizing these under-determined metabolic pathways contained in large-scale GeMs (Chapter 5). Certain SLIPs are subsequently removed based on careful consideration of their pathway functions and the implications of their removal. A majority of SLIPs have a cyclic configuration, sharing similar redox or energy co-metabolites; very few represent true conversion of substrates to products. Of the 266 under-determined SLIPs generated from the mouse GeM, only 27 SLIPs were incorporated into the final working model under the criterion that they are significant pathways and are potentially resolvable by tracer experiments. Most of these SLIPs are degradation pathways of essential amino acids and inter-conversion of non-essential amino acids (Chapter 8). In parallel, OpenFLUX was developed to perform large-scale isotopic 13C MFA (Chapter 6). This software was built to accept multiple labelled substrates, and no restriction has been placed on the model type or enrichment data. These are necessary features to support large-scale flux analysis for mammalian cells. This was followed by the development of a design strategy that uses analytical gradients of isotopomer measurements to predict resolvability of free fluxes, from which the effectiveness of various 13C experimental scenarios using different combinations of input substrates and isotopomer measurements can be evaluated (Chapter 7). Hypothetical and experimental results have confirmed the predictions that, when glucose and glutamate/glutamine are simultaneously consumed, two separate experiments using [U-13C]- and [1-13C]-glucose, respectively, should be performed. If there is a restriction to a single experiment, then the 80:20 mixture of [U-13C]- and [1-13C]-glucose can provide a better resolution than other labelled glucose mixtures (Chapter 7 and Chapter 8). The tools and framework developed in this thesis brings us within reach of performing large-scale, high-resolution fluxomics for animal cells and hence realising systems-level investigation of mammalian metabolism. Moreover, with the establishment of a more rigorous, systematic modelling approach and higher functioning computational tools, we are now at a position to validate mammalian cell culture flux experiments performed 15 years ago.

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.