Ce travail décrit l’analyse de la distribution des flux de carbones au sein de deux souches de Streptomyces coelicolor A3(2) : la souche sauvage nommée M145 et son mutant M1146 incapable de produire les antibiotiques actinorhodine, undecylprodigiosine, et l’antibiotique dépendant du calcium. Metabolite Balance Analysis et Isotopomer Balance Analysis sont mis en œuvre pour proposer un modèle de distribution des flux de carbones de S. coelicolor en phase exponentielle de croissance. Les souches M145 et M1146 sont cultivées dans un milieu minimum limitant en azote et leurs comportements métaboliques sont comparés. Dans la souche non productrice M1146, un taux de croissance plus élévé, un flux plus important dans la voie des pentoses phosphates, un flux plus faible au niveau du cycle de Krebs ainsi qu’une activité respiratoire plus faible sont mis en évidence. Cela traduit le coût énergétique important associé à la production d’actinorhodine par M145. De plus, ce travail propose un rôle important de la nicotinamide nucléotide transhydrogénase pour le maintien de l’homéostasie du NADPH lors de la production d’actinorhodine par M145. Comme il existe de bonnes corrélations entre les données expérimentales et celles issues de la modélisation au niveau des bilans carbones, des bilans de pouvoir réducteur et des échanges gazeux, il sera intéressant d’utiliser cette modélisation avec la technique de Flux Balance Analysis pour prédire les variations de la distribution des flux de carbones dans des mutants de S. coelicolor pour lesquels des gènes auraient été sur-exprimés ou délétés. / This work describes an analysis of carbon flux distribution in two strains of Streptomyces coelicolor A3(2), namely the wild type strain M145 and its derivative M1146 that is no longer able to produce the antibiotics actinorhodin, undecylprodigiosin and the calcium dependent antibiotic. Metabolite Balance Analysis and Isotopomer Balance Analysis were used to propose a model for carbon flux distribution in S. coelicolor during the exponential phase of growth. Strains M145 and M1146 were grown under nitrogen limitation in minimal medium and their metabolic behaviour were compared. In the non-producing strain M1146, a higher growth rate, a higher flux via the pentose phosphate pathway, a decreased flux through the TCA cycle and a decreased respiratory activity were evidenced. This highlighted the high energetic cost for actinorhodin production in M145. In this paper, we also propose a key role for the nicotinamide nucleotide transhydrogenase in NADPH homeostasis in M145 during actinorhodin production. As there are good correlations between experimental data and the model in terms of carbon balance, reducing power balance and gas exchanges, this model will be of great interest for Flux Balance Analysis to predict carbon-flux distribution changes in S. coelicolor strains in which gene are deleted or overexpressed.

The development of genome scale metabolic models have been aided by the increasing availability of genome sequences of microorganisms such as Geobacter sulfurreducens, involved in environmentally relevant processes such as the in-situ bioremediation of U(VI). Since microbial activities are the major driving forces for geochemical changes in the sub-surface, understanding of microbial behavior under a given set of conditions can help predict the likely outcome of potential subsurface bioremediation strategies. Hence, a model based lookup table was created to capture the variation in physiology of G. sulfurreducens in response to environmental perturbations. Thermodynamically feasible flux distributions were generated by incorporating thermodynamic constraints in the model. These constraints together with the mass balance constraints formed the thermodynamics based metabolic flux analysis model (TMFA). Metabolomics experiments were performed to determine the concentration of intracellular metabolites. These concentrations were posed as constraints in the TMFA model to improve the model accuracy.

Plasmodium falciparum is the causative agent of malaria, one of the world‟s most prevalent infectious diseases. The emergence of strains resistant to current therapeutics creates the urgent need to identify new classes of antimalarials. Here we present and analyse a constraints-based model (iMPMP427) of P. falciparum metabolism. Consisting of 427 genes, 513 reactions, 457 metabolites, and 5 intracellular compartments, iMPMP427 is relatively streamlined and contains an abundance of transport reactions consistent with P. falciparum’s observed reliance on host nutrients. Flux Balance Analysis simulations reveal the model to be predictive in regards to nutrient transport requirements, amino acid efflux characteristics, and glycolytic flux calculation, which are validated by a wealth of experimental data. Furthermore, enzymes deemed to be essential for parasitic growth by iMPMP427 lend support to several previously computationally hypothesized metabolic drug targets, while discrepancies between essential enzymes and experimentally annotated drug targets highlight areas of malarial metabolism that could benefit from further research.

Plasmodium falciparum is the causative agent of malaria, one of the world‟s most prevalent infectious diseases. The emergence of strains resistant to current therapeutics creates the urgent need to identify new classes of antimalarials. Here we present and analyse a constraints-based model (iMPMP427) of P. falciparum metabolism. Consisting of 427 genes, 513 reactions, 457 metabolites, and 5 intracellular compartments, iMPMP427 is relatively streamlined and contains an abundance of transport reactions consistent with P. falciparum’s observed reliance on host nutrients. Flux Balance Analysis simulations reveal the model to be predictive in regards to nutrient transport requirements, amino acid efflux characteristics, and glycolytic flux calculation, which are validated by a wealth of experimental data. Furthermore, enzymes deemed to be essential for parasitic growth by iMPMP427 lend support to several previously computationally hypothesized metabolic drug targets, while discrepancies between essential enzymes and experimentally annotated drug targets highlight areas of malarial metabolism that could benefit from further research.

The development of genome scale metabolic models have been aided by the increasing availability of genome sequences of microorganisms such as Geobacter sulfurreducens, involved in environmentally relevant processes such as the in-situ bioremediation of U(VI). Since microbial activities are the major driving forces for geochemical changes in the sub-surface, understanding of microbial behavior under a given set of conditions can help predict the likely outcome of potential subsurface bioremediation strategies. Hence, a model based lookup table was created to capture the variation in physiology of G. sulfurreducens in response to environmental perturbations. Thermodynamically feasible flux distributions were generated by incorporating thermodynamic constraints in the model. These constraints together with the mass balance constraints formed the thermodynamics based metabolic flux analysis model (TMFA). Metabolomics experiments were performed to determine the concentration of intracellular metabolites. These concentrations were posed as constraints in the TMFA model to improve the model accuracy.

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.

Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.

Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.

The possible benefits of thermal remediation of NAPL-contaminated subsurface were analyzed at pore-scale. Force balance analysis was performed to provide the insight and information on the critical conditions for the blob mobilization. First, the critical blob radius for blob mobilization was calculated in terms of blob radius, temperature, and water velocity. Temperature increase enhanced the blob mobilization along with the decrease of interfacial tension. Water velocity increase also enhanced the blob mobilization. Critical water velocity provided the critical condition for the initiation of blob mobilization to distinguish singlet and doublet in blob size. Second, the terminal (or steady state) blob velocity at the steady state blob motion was determined. Increases of temperature and water velocity raised the terminal blob velocity. When the observation of blob mobilization moved from REV scale (macroscale) to pore-scale, terminal blob velocity showed the different phenomena according to the change of oil saturation. At macro-scale, the terminal blob velocity was smaller than water velocity by an order or two. However, the terminal blob velocity reached to water velocity at pore-scale. This investigation would provide the better understanding on the pore-scale analysis of residual NAPL blob mobilization by thermal remediation. Additionally, the pore-scale analysis developed in this study would be incorporated into a general conservation equation in terms of the accumulation of multiple blobs. It would derive continuumaveraged equations that accurately represent pore-level physics. In conclusion, the study on the critical conditions for the initiation of blob mobilization as a single discrete blob would have some contribution to the transport and fate of NAPL contaminant and the desired subsurface remediation.

Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.