Academic literature on the topic 'Metabolic pathway modelling'

Les systèmes acellulaires sont en train de devenir une puissante plateforme de biofabrication. L'optimisation de systèmes acellulaires est importante pour obtenir un rendement maximal. L'optimisation expérimentale, en laboratoire humide, est longue et coûteuse. Différents types de modélisations permettant d'optimiser la voie d'intérêt, en un temps plus court et à moindre coût, sont apparus au cours des dernières décennies. Dans cette étude, nous avons testé deux approches : systémique à travers la mise en œuvre de réseaux de neurones, et analytique à travers l™utilisation d™équations différentielles. Dans une première étape, un modèle à réseau de neurones artificiels a été construit pour prédire le flux de métabolites à travers la voie. Dans une seconde étape, une nouvelle méthodologie, appelée GC-ANN, a été développée pour sélectionner des équilibres enzymatiques optimaux, et rentables, pour des valeurs de flux plus élevées. Cette approche a permis une amélioration inattendue du flux, jusqu'à 63%, validée in vitro. Dans une troisième étape, un modèle cinétique a été construit, et l™estimation des paramètres cinétiques pour les enzymes sélectionnées a été réalisée, afin de reproduire les conditions expérimentales. Enfin, liée à l'un des produits chimiques les plus exigeants en termes de production, la voie de synthèse du malate a été modélisée avec succès dans un système acellulaires. Même si de nombreuses études ont été réalisées, la biofabrication a grande échelle n'est pas encore possible pour le malate. La combinaison du système acellulaire et de la modélisation pourrait aider à réaliser la bioproduction du malate. De manière plus générale, cette thèse explore différentes approches de modélisations mathématiques, et leurs limites, pour l'optimisation de voies métaboliques<br>Cell-free systems (CFS) are emerging as a powerful platform for biomanufacturing. The optimisation of the cell-free system is important to achieve maximum yield. The experimental optimisation is time-consuming and expensive. Different kinds of modelling emerged in the last decades, helping to optimise the pathway of interest in a shorter time at a low cost. In this study, we tested two approaches: systemic through the implementation of neural networks, and analytical through the use of differential equations. In the first step, an artificial neural network model was built to predict the flux through the pathway, and in the second step, a new methodology termed GC-ANN was developed to select optimum and cost-efficient enzyme balances for higher flux. This approach showed unexpected betterment of flux estimation, up to 63%. In the third step, a kinetic model was built and estimation of kinetic parameters for selected enzymes was achieved to replicate experimental conditions. Finally, linked to one of the most demanding chemicals, malate synthesis pathway was successfully modelled in the cell-free system. Even though many studies have been performed, biomanufacturing has not yet been possible for malate. The combination of the cell-free system and modelling could help achieve the biomanufacturing of malate. Overall, this thesis explores different mathematical modelling approaches, and their limits, for optimising metabolic pathways

Geobacillus thermoglucosidasius is a Gram-positive thermophilic eubacterium (45-70‰) that has the ability to convert pre-treated lignocellulosic material LCM into ethanol. This organism has been genetically engineered such that its yield of ethanol production is in excess of 90% of the theoretical maximum [38]. There remains considerable scope to develop G.thermoglucosidasius to produce alternative fuels and chemicals of industrial importance. For such a useful bacterium the understanding of the global metabolism remains poorly characterised. To gain a better insight into the metabolic pathways and capabilities of G. thermoglucosidasius a bottom-up approach to construct a comprehensive metabolic model of the organism was applied. The model was build from manually annotated genome and incorporates data from wet lab experiments for accurate in silico analyses. The model simulations has highlighted a potential experimental design for the in silico production of succinate and butane-2,3-diol. PathwayBooster is also introduced in this study as a tool for curating metabolic pathways. The methodology is based on the assumption that the core metabolic capabilities are shared among evolutionarily closely related species [80]. This approach led to the further analysis of members of the genus Geobacillus with respect to their core metabolic capabilities, genome re-arrangements and shared unique features. Theoretical route for the biosynthesis of Vitamin B12 is presented here, which is novel to the canonical aerobic and anaerobic pathways known to date and ubiquitous amongst Geobacillus spp. The analysis of the gene assignment for this bacterium has highlighted the presence of NADP-dependent GAPDH. The theoretical function of this novel and previously uncategorised enzyme in the genus Geobacillus has been confirmed through enzymatic assays.

Improving efficiency within the agricultural industry is vital to maintain the food demands of the increasing population, as well the current preference for a more protein rich diet. One avenue for addressing these issues is to study animal-based growth to determine if the efficiency of the production system can be improved by increasing lean muscle mass. The aim of this thesis is to provide an alternative exploration to experimental work to provide an insight into how muscle metabolism in pigs is altered by the administration of a beta-agonist which induces muscle hypertrophy. This will be incorporated into a wider body of work to determine specific pathways to target for improving feed conversion efficiency, contributing to the necessary research into global food security. We begin by compiling a selection of statistical methods to analyse muscle microarray data, which enables the identification of a selection of genes whose expressions are altered by the exposure to a beta-agonist. These differentially expressed transcripts are then grouped via a k-means algorithm, with log likelihood and the Bayesian Inference Criterion calculations providing an optimal selection of clusters. This results in selecting a group of 51 transcripts and partitioning them into 9 clusters, and identifying several pathways which appear key to the regulation of muscle metabolism in the presence of beta-agonist. We have proceeded to incorporate this information into a mathematical model for glycolysis and the TCA cycle, in an effort to analyse biological hypotheses about how the promoters work. The equations describe the concentrations of metabolites within the cytosolic and mitochondrial compartments of a cell using mass balance ODEs. An initial model is presented, which is then increased in complexity, to keep up with developments in the experimental side of the overarching project. We make use of a selection of methods to analyse the model in an attempt to determine the effects that the different parameters cause. Through steady state analysis, we determine parameter ranges which permit positive steady states. In finding these regions, we also determine the existence of time dependent solutions, which occur when critical values of certain parameters are exceeded, and result in the build up of specific metabolites. We use asymptotic analysis to generate approximate solutions when steady states do not exist. The model parameters of most interest are those which were identified through the microarray work, namely the upregulated transcripts of PCK2 and those within the serine synthesis pathway, the control mechanism for the first half of the TCA cycle, the proportion of GTP producing enzyme from the second half of the TCA cycle, and the flux into the glycolytic pathway. We find that critical values for the glycolytic flux, and the GTP production parameter exist, determining whether the model lies within the steady state regime. In a large number of cases, the parameters we choose to represent the beta-agonist case push the system into the time dependent state. The model does not exhibit any interesting behaviour when the parameter controlling the PCK2 pathway is studied, indicating that initial intuition of the key controlling reaction mechanisms were incomplete. Whilst there are shortfalls in the model, which highlight areas for investigation, the system is set up for validation and parameter fitting when appropriate experimental data become available. We have been able to determine specific metabolic pathways within the cell which may be of significance to improving feed efficiency.

The objective of this thesis is to use metabolic modelling techniques to investigate primary and secondary metabolism in S. erythraea and from this to identify key factors controlling flux distribution during secondary metabolism. S. erythraea is a member of the actinomycetes a group of bacteria responsible for the production of a number of commercially important small molecules. Actinomycete physiology is considerably more complicated than that seen in "simple" bacteria such as E. coli. The conjecture investigated in this thesis is that metabolic modelling techniques that take into account this extra complexity should be more useful in designing strategies for overproduction of desired metabolites than simpler models. The thesis gives the first detailed description of the dynamic changes in biomass composition seen during the batch cultivation of S. erythraea. It further shows that incorporation of this information into a flux balance model of the organism's metabolism significantly improves the flux distributions generated especially in the stationary phase. Using this improved technique growth phase and stationary phase metabolism are investigated. Some of the unusual stationary phase behaviour is shown to be the result of glucose uptake being independent of demand. Rigid control of branch points in the metabolic network is not found suggesting that the organism's metabolism is flexible. A reverse metabolic engineering strategy is applied, two variants of the wild type organism are compared with an industrial strain. The industrial strain is found to have a considerably lower glucose uptake rate than the parental strain. The relationship between TCA cycle flux, oxidative phosphorylation and organic acid secretion is investigated using an uncoupler. This project demonstrates that applied correctly flux balance analysis is a powerful tool for investigating actinomycete physiology. The insights gained are of direct relevance to the commercial production of secondary metabolites in S. erythraea.