The aim of this thesis was to explore the use of genome-scale metabolic models to predict metabolic fluxes in plant tissues. Results from this thesis showed that the application of constraint-based modelling, namely flux balance analysis, to an Arabidopsis genome-scale metabolic model gave accurate predictions of metabolic fluxes in heterotrophic cell culture and in photosynthetic leaves. Two major factors important for the accuracy of model predictions were highlighted from the study: 1) the inclusion of energetic costs for transports and cellular maintenance in terms of ATP and NADPH; 2) consideration of the interactions between light and dark metabolism in modelling photosynthetic leaves. This study began with the construction of a well-curated and compartmented genome-scale metabolic model of Arabidopsis. Using the model, cellular maintenance costs in a heterotrophic cell culture under control and two stress conditions were estimated in terms of ATP and reductant usage. The results suggested that the cells were not stressed under hyperosmotic conditions. Comparisons between model predictions and experimentally estimated flux maps showed that the inclusion of transport and maintenance costs was important for obtaining accurate model flux predictions. To model leaf metabolism over a day-night cycle, a diel modelling framework was developed which took into account the interactions between light and dark metabolism. Numerous known features of metabolism in a C₃ leaf were predicted such as the nocturnal accumulation of citrate utilised for diurnal glutamate and glutamine synthesis and the operation of an incomplete TCA cycle during the day. Using the Arabidopsis genome-scale metabolic model and the diel modelling framework, the operation of the CAM cycle was predicted as a direct consequence of blocking the CO₂ exchange with the external air during the day to simulate closure of the stomata. Comparisons between model predictions of C₃ and various subtypes of CAM leaves suggested that photon and nitrogen use efficiencies are unlikely to be the driving forces for the evolution of CAM plants under the current atmospheric CO₂ concentration. Finally, the model was utilised to predict the changes in metabolic fluxes, in particular fluxes through various routes of alternative electron flow, in a C₃ leaf with varying light intensity, nitrogen availability and at different stages of leaf development. From the model flux predictions, it was shown that constraint-based modelling can be utilised to elucidate the distinct metabolic roles of enzymes in different subcellular compartments and the tissue-specific use of distinct forms of enzymes with different coenzyme specificities.

The potential value of the chitin biomass (e.g. food waste) is recently considered being ignored by landfill. Chitin can be a potential cheap carbon source for converting into value-added chemicals by microorganisms. Serratia marcescens is a chitinolytic bacterium that harbors endogenous chitinase systems. With goals of characterzing S. marcescens chitinolytic capabilities and applying S. marcescens to chemical production from chitin, my dissertation main content includes five chapters: 1) Chapter 1 highlights background information of chitin source, S. marcescens and potential metabolic engineering targets using chitin as a substrate; 2) Chapter 2 demonstrates that ChiR is a key regulator in regulating 9 chitinase-related genes in S. marcescens Db11 and manipulation of chiR can be a useful and efficient genetic target to enhance chitin utilization; 3) Chapter 3 reports the production of N-acetylneuraminic acid (Neu5Ac) from chitin by a bottom-up approach of engineering the nonconventional chitinolytic bacterium, Serratia marcescens, including native constitutive promoter characterization and transcriptional and translational pathway balancing; 4) Chapter 4 describes improvement of S. marcescens chitinolytic capability by an adaptive evolution approach; 5) Chapter 5 elucidates S. marcescens intracellular metabolite profile using a constraint-based genome-scale metabolic model (iSR929) based on genomic annotation of S. marcescens Db11. Overall, the dissertation work is the first report of demonstrating the concept of chitin-based CBP using S. marcescens and the computational model and genetic molecular tools developed in this dissertation are valuable but not limited to design-build-test of S. marcescens for contributing to the field of biological science and metabolic engineering applications.

Made available in DSpace on 2016-06-02T19:55:37Z (GMT). No. of bitstreams: 1 5536.pdf: 2735660 bytes, checksum: 14dceb0da38443c19f1b8c8410041cad (MD5) Previous issue date: 2013-09-20 / Universidade Federal de Minas Gerais / The use of fossil fuels has a significant impact on the environment, making biofuels a renewable and friendly alternative. Brazil, as one of the leading producers of sugar and ethanol, generates as main residue sugar cane bagasse, which is usually burned for power generation. However, this biomass can be reused as raw material for the production of second generation bioethanol (2G). The consolidation of the industrial production of second-generation (2G) bioethanol relies on the improvement of the economics of the process. Thus, it is important the use of both the fermentable fractions present in sugarcane bagasse, cellulose (C6) and hemicellulose (C5), for the economically feasible process. Within this general scope, the second chapter of this thesis addresses one aspect that impacts the costs of the biochemical route for producing 2G bioethanol: defining optimal operational policies for the reactor running the enzymatic hydrolysis of the C6 biomass fraction. A simple Michaelis Menten pseudo-homogeneous kinetic model with product inhibition was used in the dynamic modeling of a fed-bath reactor, and two feeding policies were implemented and validated in bench-scale reactors processing pre-treated sugarcane bagasse. The first policy was defined with the purpose of sustaining high rates of glucose production, adding enzyme (Accellerase® 1500) and substrate simultaneously during the reaction course. The second approach applied classical optimal control theory, for determining optimal substrate feeding profiles, in order to maximize the performance index proposed. Economical criteria were used for comparing the reactor performance operating in successive batches and in fed-batch modes. Fed-batch mode was less sensitive to enzyme prices than successive batches. Process intensification in the fed-batch reactor led to final glucose concentrations around 200 g/L. The third chapter, in turn, focuses on the xylose utilization, the main sugar found in the C5 fraction, for fermentation to ethanol by yeast Saccharomyces cerevisiae. Although this yeast is not capable of fermenting xylose, it is able to ferment D-xylulose obtained by isomerisation of xylose by glucose isomerase enzyme, generating ethanol and/or xylitol as the main products. The optimization of ethanol production requires the analysis of the metabolism of xylulose. In this context, the genome-scale metabolic model iND750 was adjusted. In silico experiments were carried out using the software OptFlux and compared with experimental data of batch cultivation of S. cerevisiae, in order to validate the model and establishing relationships between fluxes of assimilating xylulose and oxygen and selectivity in the production of ethanol compared to xylitol. Experiments of simultaneous isomerization and fermentation (SIF) of xylose were carried out in a continuous bioreactor containing alginate pellets as biocatalyst with enzyme glucose isomerase and S. cerevisiae coimobilizated. Final concentrations of 6 g/L of ethanol and 5 g/L of xylitol were achieved in continuous cultivation. / A utilização de combustíveis fósseis tem um significativo impacto no meio ambiente, tornando os biocombustíveis uma alternativa renovável e ambientalmente amigável. O Brasil, por ser um dos principais produtores de açúcar e etanol, gera como principal subproduto dessa indústria, o bagaço de cana de açúcar, que é geralmente queimado para geração de energia. Entretanto, esta biomassa pode ser reaproveitada como matéria-prima para produção de bioetanol de segunda geração (2G). A consolidação da produção industrial de bioetanol 2G baseia-se na melhoria econômica do processo. É importante, assim, o uso de ambas as frações fermentáveis presentes no bagaço de cana, de celulose (C6) e de hemicelulose (C5), para viabilizar economicamente o processo. Neste âmbito geral, o segundo capítulo desta tese de doutorado aborda um aspecto que impacta os custos da rota bioquímica para a produção de bioetanol 2G: definição de políticas operacionais ótimas para um reator de hidrólise enzimática da fração C6 do bagaço de cana de açúcar. Foi utilizado um modelo cinético de Michaelis-Menten pseudohomogêneo, com inibição pelo produto, na modelagem dinâmica de um reator em batelada alimentada e duas políticas de alimentação foram implementadas e validadas em reatores de escala de bancada processando bagaço de cana pré-tratado. A primeira política de alimentação foi definida com a finalidade de sustentar elevadas taxas de produção de glicose, adicionando enzima (Accellerase® 1500) e substrato simultaneamente durante o curso da reação. A segunda política aplica a teoria clássica de controle ótimo, para determinação de perfis ótimos de alimentação de substrato, a fim de maximizar o índice de desempenho proposto. Foram usados critérios econômicos para comparar o desempenho do reator operando em bateladas sucessivas e em modos de batelada alimentada. O modo batelada alimentada foi menos sensível a alterações no preço da enzima do que bateladas sucessivas. Intensificação do processo em batelada alimentada conduziu a concentrações finais de glicose de cerca de 200 g/L. Já o terceiro capítulo foca na utilização da xilose, principal açúcar encontrado na fração C5, para fermentação a etanol pela levedura Saccharomyces cerevisiae. Embora esta levedura seja incapaz de fermentar xilose, pode fermentar a D-xilulose obtida pela isomerização de xilose pela enzima glicose isomerase, gerando etanol e/ou xilitol como produtos principais. A otimização da produção de etanol requer a análise do metabolismo da xilulose. Neste contexto, o modelo metabólico em escala genômica iND750 foi utilizado e ajustado. Experiências in silico usando o software OptFlux foram realizadas e comparadas com dados experimentais de cultivos em batelada de S. cerevisiae, com o propósito de validar o modelo e estabelecer relações entre os fluxos de assimilação de xilulose e de oxigênio e a seletividade na produção de etanol em relação a xilitol. Experimentos de isomerização e fermentação simultâneas da xilose (SIF) foram realizados em reator contínuo de leito fixo, contendo como biocatalisador pellets de alginato com enzima glicose isomerase e S. cerevisiae coimobilizadas. Concentrações finais de 6 g/L de etanol e 5 g/L de xilitol foram alcançadas em cultivo contínuo.

Mycobacterium tuberculosis, the etiological agent of tuberculosis, has adapted with the host environment and evolved to survive in harsh conditions in the host. The pathogen has successfully evolved strategies not only to evade the host immune system but also to thrive within the host cells. Upon infection, the pathogen is either cleared due to the host immune response, or it survives and causes active tuberculosis (TB) infection. In a number of cases however, the pathogen is neither killed nor does it actively proliferate, but it remains dormant in the host until the environment becomes favorable. This dormant state of pathogen is responsible for latent TB infection (LTBI). WHO reports indicated that as much as a third of the whole world’s population is exposed to the pathogen, of which a significant proportion could be latently infected (WHO report, 2015). These individuals do not show symptoms of active TB infection and hence are difficult to detect. The latent TB infected (LTBI) individuals serve as a reservoir for the pathogen, which can lead to epidemics when the conditions change. Hence, it is necessary to understand the host -pathogen interactions during LTBI, as this might provide clues to developing new strategies to detect and curb a latent infection. Host-pathogen interactions are multifaceted, in which both species attempt to recognize and respond to each other, all of these through specific molecules making distinct interactions with the other species. The outcome of the infection is thus decided by a complex set of host-pathogen interactions. The complexity arises since a large number of molecular components are involved, also multiplicity of interactions among these components and due to several feedback, feed forwards or other regulatory or influential loops within the system. The complexity of biological systems makes modeling and simulation an essential and critical part of systems– level studies. Systems biology studies provide an integrated framework to analyze and understand the function of biological systems. This work addresses some of these issues with an unbiased systems-level analysis so as to identify and understand the important global changes both in the host and in the pathogen during LTBI. The broad objectives of the work was to identify the key processes that vary in the host during latent infection, the set of metabolic reactions in the host which can be modulated to control the reactivation of infection, global adaptation in Mycobacterium tuberculosis (Mtb) and then to utilize this knowledge to identify strategies for tackling latent infection. A review of literature of the current understanding of latency from the pathogen and the host perspective is described in chapter 1. From this, it is clear that most available studies have focused on the role of individual molecules and individual biological processes such as granuloma formation, toll-like receptor signaling, T cell responses as well as cytokine signaling, in either initiating or maintaining a latent infection, but there is no report till date about whether and how these processes are connected with each other. While transcriptome based studies have identified lists of differentially expressed genes in LTBI as compared to healthy controls, no further understanding is currently available for many of them, regarding the processes they may be involved in and what interactions they make, which may be important for understanding LTBI. The first part of the work is a systematic meta-analysis of genome-scale protein interaction networks rendered condition-specific with transcriptome data of patients with LTBI, which has provided a global unbiased picture of the transcriptional and metabolic variations in the host and in the pathogen during the latent infection. To start with, publicly available gene expression data related to LTBI, active TB and healthy controls were considered. In all, 183 datasets summing up to 105 LTBI, 41 active TB and 37 healthy control samples were analyzed. (Chapter 2). Standard analysis of the transcriptome profiles of these datasets indicated that there was zero overlap among them and that not a single gene was seen in common among all datasets for the same condition. An extensive human protein-protein interaction network was constructed using information available from multiple resources that comprehensively contained structural or physical interactions and genetic interactions or functional influences. Nodes in this network represented individual proteins and edges represented interactions between pairs of nodes. The identity of each node and the nature of interaction of each edge along with the type of evidence that was used as the basis for drawing the edge, was collated for the network. The gene expression data was integrated into the human protein-protein interaction (PPI) network for each condition, which essentially had weighted nodes and directed edges, specific to that condition, from which specific comparative networks were derived. The highest ranked perturbations in LTBI were identified through a network mining protocol previously established in the laboratory. This involved computing all versus all shortest paths on the comparative network, scoring the paths based on connectedness and various centrality measures of the nodes and the edges and finally ranking the paths based on the cumulative path scores. Intriguingly, the top-ranked set of perturbations were found to form a connected sub-network by themselves, referred to as a top perturbed sub-network (top-net), indicating that they were functionally linked or perhaps even orchestrated in some sense. Th17 signaling appears to be dominant. About 40 genes were identified in the unique set of LTBI condition as compared to the active TB condition, and these genes showed enrichment for processes such as apoptosis, cell cycle as well as natural killer cell mediated toxicity. Construction and analysis of a miRNA network indicated that 32 of these have strong associations with miRNA explaining the role of the latter in controlling LTBI. 3 other genes from the top-net are already established drug targets for different diseases with known drugs associated with them, which are BCL2, HSP90AA1 and NR3C1. These 3 proteins can be explored further as drug targets in LTBI whose manipulation using existing drugs may result in inhibiting the underlying biological process and thereby result in disturbing the state of latency. As a second objective, global variations in the host transcriptome were identified during ascorbic acid induced dormancy (Chapter 3). Ascorbic acid or Vitamin C is a nutrient supplement required in the diet. This organic compound has a known antioxidant property, as it is known to scavenge the free radicals. In a recent study, Taneja et al, demonstrated that Vitamin C could induce dormancy in Mtb. On similar lines, experiments were done in THP-1 cells infected with Mtb to determine the host responses during ascorbic acid (AA) induced dormancy. The raw gene expression data was provided by our collaborator Prof. Jaya Tyagi that included 0 hour, 4 days and 6 days time points with infection and vitamin C versus infection alone or vitamin C alone as controls. The transcriptome data was normalized and integrated into the human PPI network as described for the meta-analyses. It was experimentally determined that ascorbic acid induces dormancy in 4 days post infection. The top-ranked paths of perturbation were analyzed and compared for three different conditions: (i) uninfected condition, (ii) AA treated and infected condition, and (iii) AA, isoniazid and infected condition. The dormant pathogen is known to be drug-tolerant and thus as a marker for the state of dormancy, the lack of effect of isoniazid is also monitored in the infected host cells. The analysis revealed that there were some broad similarities as compared to LTBI from patient samples but AA induced dormancy in cell lines stood out a separate group indicating that there were significant differences such as involving Interferon Induced Transmembrane Proteins (IFITMs), vacuolar ATPase as well as GDF15, which belongs to TGF-beta signaling pathway. The highest ranked perturbed paths contained genes involved in innate immune responses of which ISG15, IFITMs, HLAs and ATPases emerge as the most altered in the dormant condition. CCR7 emerges as a key discriminator, which is subdued in the latent samples but highly induced in infection conditions. Pathway-based analysis of different conditions showed that oxidative stress, glutathione metabolism, proteasome degradation as well as type II interferon signaling are significantly up-regulated in AA induced dormancy. The dormant bacteria reside in the host cells and are known to modulate the host metabolism for their own benefit. So, the third objective was to understand the metabolic variations in the host during LTBI (Chapter 4). A genome-scale metabolic (GSM) model of alveolar macrophage was used in this study. The metabolic model contains information of the reactions, metabolites and the genes encoding enzymes that catalyze a particular reaction. Flux balance analysis (FBA), a constraint-based metabolic modeling method, is used for analyzing the alterations in the metabolism under different infection conditions. In order to mimic the physiological condition, gene expression data was used for constraining the bounds of the reactions in the model. Two different expression studies were used for analysis: GSE25534 (from Chapter 2) and ascorbic acid induced dormancy (Chapter 3). The analysis was carried out for latent TB versus healthy control and latent TB versus active TB to identify the most altered metabolic processes in LTBI. Differences in fluxes between the two conditions were calculated. A new classification scheme was devised to categorize the reactions on the basis of flux differences. In this chapter, higher fluxes in LTBI condition were identified for reactions involved in transport of small metabolites as well as amino acids. Solute carrier proteins responsible for the transport of the metabolites were identified and their biological significance is discussed. Reduced glutathione (GSH), arachidonic acid, prostaglandins, pantothenate were identified as important metabolites in LTBI condition and their physiological role has been described. Sub-system analysis for different conditions shows differential regulation for arachidonic acid metabolism, fatty acid metabolism, folate metabolism, pyruvate metabolism, glutathione metabolism, ROS detoxification, triacylglycerol synthesis and transport as well as tryptophan metabolism. From the study, transporter proteins and reactions altered during LTBI were identified, which again provide clues for understanding the molecular basis of establishing a latent infection. Mycoabcterium tuberculosis is known to undergo dormancy during stress conditions. In this chapter, the main objective was to identify the global variations in the dormant Mtb (Chapter 5). To carry out the analysis, the Mtb PPI network was constructed using information from available resources. Gene expression data of two different dormancy models, Wayne growth model and multiple-stress model, were used for the study. To identify the key players involved in reversal of dormancy, the transcriptome data of reaeration condition was also used. In this study, the Max-flow algorithm was implemented to identify the feasible paths or flows in different condition. The flows with higher scores indicate that more information is traversed by the path, and hence is important for the study. From the analysis of Wayne growth model (hypoxia model), important transcriptional regulators such as SigB, SigE, SigH, regulators in the two-component system such as MprA, MtrA, PhoP, RegX3 and TrcR were identified in stress condition. Multiple-stress model studied the growth of bacteria in low oxygen concentration, high carbon dioxide levels, low pH and nutrient starvation. The gene expression data was integrated in the Mtb PPI network and implementation of Max-flow algorithm showed that MprA, part of the MprA-MprB two-component system, is involved in the regulation of persistent condition. WhiB1 also features in the paths of dormant condition and its role in persistence can be explored. In reaeration model, WhiB1 and WhiB4 are present in the top flows of this condition indicating that the redox state is perturbed in the pathogen and the interactions of these proteins are important to understand the reversal of dormant condition. From the study, Rv2034, Rv2035, HigA, Rv1989, Rv1990 and Rv0837 proteins belonging to toxin-antitoxin systems were also identified in the dormant bacteria, indicating their role in adaptation during stress condition. The role of Rv2034 has been studied in persistence, but the function of other proteins can be analyzed to provide new testable hypotheses about the role of these proteins in dormancy. Thus, the flows or paths perturbed during dormancy were identified in this study. To get a better understanding of the metabolic network active in mycobacteria under different conditions, experiments were performed in Mycobacterium smegmatis MC2 155. The non-pathogenic strain of genus Mycobacteria, Mycobacterium smegmatis, is used as a surrogate to carry out molecular biology studies of Mtb. Mycobacterium smegmatis MC2 155 (Msm) is the commonly used laboratory strain for experimental purpose. In order to obtain a clear understanding of how comparable are the metabolic networks between the virulent M. tuberculosis H37Rv and the model system Msm, the latter model is first studied systematically. In Chapter 6, first the functional annotation of the Msm genome was carried out and the genes were categorized into different Tuberculist classes based on homology with the Mtb genome. A high-throughput growth characterization was carried out to characterize the strain systematically in terms of different carbon, nitrogen or other sources that promoted growth and thus served as nutrients and those that did not, together yielding a genome-phenome correlation in Msm. Gene expression was measured and used for explaining the observed phenotypic behavior of the organism. Together with the genome sequence, the transcriptome and phenome analysis, a set of about 257 different metabolic pathways were identified to be feasible in wild-type Msm. About 284 different carbon, nitrogen source and nutrient supplements were tested in this experiment and 167 of them supported growth of Msm. This indicates that the compounds enter the cells and are metabolized efficiently, thus yielding similar phenotypes. The expressed genes and metabolites supporting growth were mapped to the metabolic network of Msm, thus helping in the identification of feasible metabolic routes in Msm. A comparative study between Msm and Mtb revealed that these organisms share similarity in the nutrient sources that are utilized for growth. The study provides experimental proof to identify the feasible metabolic routes in Msm, and this can be used for understanding the metabolic capability in the two organisms under different conditions providing a basis to understand adaptations during dormancy. In the last part of the work presented in this thesis, the metabolic shift in the pathogen was studied using a genome-scale metabolic model of Mtb (Chapter 7). The model contains information of the reactions, metabolites and genes involved in the reactions. Flux balance analysis (FBA) was carried out by integrating normalized gene expression data (Wayne model and multiple-stress model transcriptome considered in Chapter 5) to identify the set of reactions, which have a higher flux in the dormant condition as compared to the control replicating condition. Glutamate metabolism along with propionyl CoA metabolism emerge as major up-regulated processes in dormant Mtb. Next, with an objective of identifying essential genes in dormant Mtb, a systematic in silico single gene knock-out analysis was carried out where each gene and it's associated reaction was knocked out of the model, one at a time and the ability of the model to reach its objective function assessed. About 168 common genes in Wayne model and multiple-stress model were identified as important in Mtb after the knockout analysis. Essentiality is in essence a systems property and requires to be probed through multiple angles. Towards this, essential genes were identified in Mtb using a multi-level multi-scale systems biology approach. About 283 genes were identified as essential on the basis of combined analysis of transcriptome data, FBA, network analysis and phyletic retention studies in Mtb. 168 genes identified as important in dormant Mtb were compared with 283 essential genes and about 91 genes were found to be essential. Finally, among the set of essential genes, those that satisfy other criteria for a drug target were analyzed using the list of high-confidence drug targets of Mtb available in the laboratory along with their associated drug or drug-like molecules. 38 out of the 168 important genes in Mtb were found to have one or more drugs associated with them from the DrugBank database. Colchicin-Rv1655, Raloxifene-Rv1653, Bexarotene-Rv3804, Rosiglitazone-Rv3804 are top-scoring drug-target pairs that can be explored for killing dormant bacilli. The study has thus been useful in identifying important proteins, reactions and drug targets in dormant Mtb. In summary, the thesis presents a comprehensive systems-level understanding of various aspects of host responses and pathogen adaptation during latent TB infection. Key host and pathogen factors involved in LTBI are identified that serve as useful pointers for deriving strategies for tackling a latent infection.

Esta tesis se centra en la construcción y usos de los modelos metabólicos a escala genómica para obtener biocombustibles de manera eficiente, como etanol e hidrógeno. Como organismo objetivo, se ha elegido a la cianobacteria Synechocystis sp. PCC6803. Este organismo ha sido estudiado como una potencial plataforma de producción alimentada por fotones, dada su capacidad de crecer solamente a partir de dióxido de carbono y fotones. Esta tesis versa acerca de los métodos para modelar, analizar, estimar y predecir el comportamiento del metabolismo de las células. La principal meta es extraer conocimiento de los diferentes aspectos biológicos de un organismo con el fin de utilizarlo para un objetivo industrial pertinente. Esta tesis ha sido estructurada en capítulos organizados de acuerdo con las sucesivas tareas que terminan con la construcción de una célula in silico que se comporta, idealmente, como la que está basada en el carbono. Este proceso suele comenzar con los archivos de anotación del genoma y termina con un modelo metabólico a escala genómica capaz de integrar datos -ómicos. El primer objetivo de la presente tesis es la reconstrucción de un modelo del metabolismo de esta cianobacteria que tenga en cuenta todas las reacciones presentes en la misma. Esta reconstrucción tenía que ser lo suficientemente flexible como para permitir el crecimiento en las distintas condiciones ambientales bajo las cuales este organismo crece en la naturaleza, así como permitir la integración de diferentes niveles de información biológica. Una vez que se cumplió este requisito, se pudieron simular variaciones ambientales y estudiar sus efectos desde una perspectiva de sistema. Se han estudiado hasta cinco diferentes condiciones de crecimiento en este modelo metabólico y sus diferencias han sido evaluadas. La siguiente tarea fue definir estrategias de producción para sopesar la viabilidad de este organismo como una plataforma de producción. Se simularon perturbaciones genéticas para e / This thesis is focused on the construction and uses of genome-scale metabolic models to efficiently obtain biofuels, such as ethanol and hydrogen. As a target organism, cyanobacterium Synechocystis sp. PCC6803 was chosen. This organism has been studied as a potential photon-fuelled production platform, for its ability to grow only from carbon dioxide, water and photons. This dissertation verses about methods to model, analyse, estimate and predict the metabolic behaviour of cells. Principal goal is to extract knowledge from the different biological aspects of an organism in order to use it for an industrial relevant objective. This dissertation has been structured in chapters accordingly organized as the successive tasks that end up building an in silico cell that behaves as the carbon-based one. This process usually starts with the genome annotation files and ends up with a genome-scale metabolic model able to integrate ¿omics data. First objective of present thesis is to reconstruct a model of this cyanobacteria¿s metabolism that accounts for all the reactions present in it. This reconstruction had to be flexible enough as to allow growth under the different environmental conditions under which this organism grows in nature as well as to allow the integration of different levels of biological information. Once this requisite was met, environmental variations could be simulated and their effect studied under a system-wide perspective. Up to five different growth conditions were simulated on this metabolic model and differences were evaluated. Following assignment was to define production strategies to weigh this organism¿s viability as a production platform. Genetic perturbations were simulated to design strains with an enhanced production of three industrially-relevant metabolites: succinate, ethanol and hydrogen. Resulting sets of genetic modifications for the overproduction of those metabolites are, thus, proposed. Moreover, functional reactions couplings were studied and weighted to their metabolite production importance. Finally, genome-scale metabolic models allow establishing integrative approaches to include different types of data that help to find regulatory hotspots that can be targets of genetic modification. Such regulatory hubs were identified upon light/dark shifts and general metabolism operational principles inferred. All along this process, blind spots in Synechocystis sp. PCC6803 metabolism, and more importantly, blind spots in our understanding of it, are revealed. Overall, the work presented in this thesis unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean production platform. / Esta tesis es centra en la construcció i els usos del models metabòlics a escala genòmica per a obtenir eficientment biocombustibles, com etanol i hidrogen. Com a organisme diana, s¿elegí el cianobacteri Synechocystis sp. PCC6803. Aquest organisme ha segut estudiat com una plataforma de producció nodrida per fotons, per la seva habilitat per créixer a partir únicament de diòxid de carboni, aigua i fotons. Aquesta tesi versa sobre mètodes per a modelitzar, analitzar, estimar i predir el comportament metabòlic de cèl¿lules. La principal meta és extreure coneixement del diferents aspectes biològics d¿un organisme de manera que s¿usen per a un objectiu industrial rellevant. La tesi ha segut estructurada en capítols organitzats d¿acord a les successives tasques que acaben construint una cèl¿lula in silico que es comporta, idealment, com la que està basada en carboni. Aquest procés generalment comença amb els arxius de l¿anotació del genoma i acaba amb un model metabòlic a escala genòmica capaç d¿integrar dades ¿òmiques. El primer objectiu de la present tesi és la reconstrucció d¿un model del metabolisme d¿aquest cianobacteri que tinga en compte totes les reaccions que hi estan presents. Esta reconstrucció havia de ser prou flexible com per permetre la simulació del creixement en les diferents condicions ambientals en les quals aquest cianobacteri creix en la natura, així com permetre la integració de diferents nivells d¿informació biològica. Una vegada que aquest requisit fou assolit, es pogueren simular variacions ambientals i estudiar els seus efectes amb una perspectiva de sistema. S¿han simulat fins a cinc condicions de creixement en este model metabòlic i les seves diferències han segut avaluades. La següent tasca fou definir estratègies de producció per a valorar la viabilitat d¿aquest organisme com a plataforma de producció. Es simularen pertorbacions genètiques per al disseny de soques amb producció millorada de metabòlits de rellevància industrial: succinat, etanol i hidrogen. Així, es proposen conjunts de modificacions genètiques per a la sobreproducció d¿aquests metabòlits. També s'han estudiat reaccions acoblades funcionalment i s¿ha ponderat la seva importància en la producció de metabòlits. Finalment, els models metabòlics a escala genòmica permeten establir criteris per integrar diferents tipus de dades que ens ajuden a trobar punts importants de regulació. Eixos centres reguladors, que poden ser objecte de modificacions genètiques, han segut investigats baix canvis dràstics d¿il¿luminació i s¿han inferit principis operacionals del metabolisme. Al llarg d'aquest procés, s¿han revelat punts cecs al metabolisme de Synechocystis sp. PCC6803 i, el més important, punts cecs en la nostra comprensió d'aquest metabolisme. En general, el treball presentat en aquesta tesi dona a conèixer les capacitats industrials del cianobacteri Synechocystis sp. PCC6803 per a produir metabòlits d'interès, tot sent una plataforma de producció neta i sostenible. / Montagud Aquino, A. (2012). Modelling and analysis of biological systems to obtain biofuels [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17319 / Palancia