There is a global need for sustainable, renewable and clean energy sources. Microbial production of hydrogen from renewable carbon sources, biorefinery compounds such as succinic acid or from food and drinks industry waste meets all these criteria. Although it has been studied for several decades, there is still no large scale bio-hydrogen production because the rate and yield of hydrogen production are not high enough to render the process economical. The dependency of biological hydrogen production of incipient light energy is also an important factor affecting economics. In order to improve the prospects of biohydrogen as a renewable and sustainable energy alternative, the genetic and process engineering approaches should be helped and targeted by metabolic engineering tools such as metabolic flux balance analysis. The overall aim of this research was the development of computational metabolic flux balance analysis for the study of growth and hydrogen production in Rhodobacter sphaeroides. The research reported in this thesis had two approaches; experimental and computational. Batch culture experiments for growth and hydrogen production by Rhodobacter sphaeroides were performed with either malate or succinate as carbon source and with glutamate as the nitrogen source. Other conditions investigated included; i) aerobic and anaerobic growth, ii) light and dark fermentation for growth, and iii) continuous light and cycled light/dark conditions for hydrogen production. The best growth was obtained with succinate under anaerobic photoheterotrophic conditions with the maximum specific growth rate of 0.0467 h– 1, which was accompanied with the maximum specific hydrogen production rate of 1.249 mmol(gDW.h)– 1. The range of the photon flux used was 5.457 - 0.080 mmol(gDW.h)– 1. The metabolic flux balance model involved 218 reactions and 176 metabolites. As expected the optimised specific rates of growth and hydrogen production were higher than those of the experimental values. The best prediction was for hydrogen production on succinate with computed specific hydrogen production rates in the range of 2.314 - 1.322 mmol(gDW.h)– 1. Sensitivity analyses indicated that the specific growth rate was affected by the nitrogen source uptake rate under aerobic dark condition whereas the flux of protein formation had the largest effect on the specific growth rate under anaerobic light condition.

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.

Systems metabolic engineering has enabled systematic studying of microbes for modifying their genetic contents, analyzing their metabolism, and designing new capabilities. One of the most commonly used approaches in systems metabolic engineering involves genome-scale metabolic flux modeling. These models allow generation of predictions of the global metabolic flux distribution in the metabolic network of organisms, in silico. With the current advances in genome sequencing technologies and the global demand for bio-based commodity chemicals and fuels, genome-scale models can help metabolic engineers propose design strategies while considering holistic behavior of the organism. In this research, novel tools and methodologies were developed to improve the future prospective of systems metabolic engineering with genome-scale modeling. To do this, an online web application (Synthetic Metabolic Pathway Builder and Genome-Scale Model Database, SyM- GEM) was first developed enabling the construction of synthetic metabolic pathway(s) and addition of those to synchronized genome-scale models. This addresses the need for an easy and universal way of creating models of engineered microbes with improved properties without the time-consuming inconvenience of synchronizing different formats and representations of genome- scale models prepared by different laboratories. The web application is freely available at http:www.mesb.bse.vt.edu/SyM-GEM. Then, a computational framework (Total Membrane Influx-Flux Balance Analysis, ToMI-FBA) was developed to allow for evaluating synthetic pathway use by different models. This enabled, for the first time, a computational guide for optimal host selection (for a specific metabolic engineering problem) and culture media formulation design to achieve the solution. Results showed that (i) L-valine improves isobutanol production by Bacillus subtilis, (ii) cellobiose increases ethanol selectivity by Clostridium acetobutylicum ATCC 824, and (iii) B. subtilis is an optimal host for artimisinate production. To further expand the capability of genome-scale models, an algorithm was developed (Genetic Algorithm-Flux Balance Analysis minimizing Total Unconstrained eXchange Flux, GA-FBA minimizing TUX) to help improve the fitness between metabolic fluxes predicted by genome-scale modeling and those obtained by 13C-tracing methods. Application of this method to the cyanobacterium Synechocystis PCC 6803 improved model accuracy by more than 50% for both heterotrophic and autotrophic growth. To generate even more realistic predictions of metabolic flux from genome-scale modeling, Raman spectroscopy was employed to help design biomass equations of microbial cells in different environmental conditions. To do this, the cellulose- consuming anaerobe Clostridium cellulolyticum ATCC 35319 was grown on cellobiose, and samples were obtained at different points of differentiation due to sporulation. Biomass composition was determined through Raman spectroscopy and traditional chemical analyses. A new genome-scale model of this organism (iCCE557) served as the basis for genome-scale model calculations. Model fitness improved upto 95% with these methods. Finally, to implement metabolic engineering strategies, regulatory RNA molecules (antisense RNAs) were designed to help target desired mRNA molecules in the metabolic network. Thermodynamic binding calculations were found to correlate with the efficiency of asRNA-mRNA binding and inhibition of mRNA translation. / Ph. D.

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.

Mammalian cell culture has gained importance in biotechnology for the development of therapeutic and diagnostic agents. Among them, Chinese hamster ovary (CHO) cells are regarded as the mammalian cell "workhorse". The use of CHO cell line for the production of recombinant proteins used in human therapy has reached a level of industrial production. However, a major problem encountered in in vitro cultures is cell death via apoptosis. Since apoptosis leads to the loss of viability of mammalian cells in vitro, especially in serum-free media. This is important and necessary to prevent the activation of apoptosis cascade and increase their cell viability and enhance their cellular robustness. The overall goal of this study is to improve our understanding of the cellular and physiological determinants of apoptosis and its relationship with other cellular functions. Apoptosis is a result of a very complex network of signaling pathways triggered from both inside and outside of the cell and a highly regulated pathway by both pro-apoptotic and anti-apoptotic proteins that promote cell survival or cell death. Although many causes of apoptotic process in mammalian cell cultures had been researched in the past and have been discussed in recent years, a lot need to be explored. In order to bring novel strategies to understand apoptosis in mammalian cell cultures, our study was not only focused on the apoptotic pathway but also expand to metabolic network to set up a link between cell growth and apoptosis. In our project, we applied systems biology methods in a mammalian cell line (CHO TF 70R), to understand the relationship between cellular metabolism and apoptosis in a typical serum free culture medium. After establishing the basic culture platform, the effects of culture conditions on initiating apoptosis will be evaluated. Healthy and apoptotic cell samples were identified and isolated using Fluorescence Activated Cell Sorting (FACS) and Magnetic Activated Cell Sorting (MACS), respectively. A comprehensive study of CHO cellular metabolism was made using a metabolic flux network to compare and analyze by metabolic flux analysis (MFA) to get more information on cell metabolism and apoptotic behavior. Furthermore, 2-NBDG combined with Annexin V-PE was also successfully applied to estimate the glucose uptake rate in real early apoptotic cells. In summary, we used the integration of the data generated by MFA to understand apoptotic behavior and establish a correlation between cell regulation and apoptosis. It will help us to identify the changes during the onset of apoptosis process will be studied by using proteomics tools to analyze the protein up-regulation or down-regulation in different cell status in the future.

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.