Genome-scale metabolic reconstructions are an important resource in the study of metabolism. They provide both a system and component level view of the biochemical transformations of metabolites. As more reconstructions have been created it remains a challenge to integrate and reason about their contents. This thesis focuses on the development of computational methods to allow on-demand comparison and alignment of metabolic reconstructions. A novel method is introduced that utilises chemical structure representations to identify equivalent metabolites between reconstructions. Using a graph theoretic representation allows the identification and reasoning of metabolites that have a non-exact match. A key advantage is that the method uses the contents of reconstructions directly and does not rely on the creation or use of a common reference. To annotate reconstructions with chemical structure representations an interactive desktop application is introduced. The application assists in the creation and curation of metabolic information using manual, semi-auto\-mated, and automated methods. Chemical structure representations can be retrieved, drawn, or generated to allow precise metabolite annotation. In processing chemical information, efficient and optimised algorithms are required. Several areas are addressed and implementations have been contributed to the Chemistry Development Kit. Rings are a fundamental property of chemical structures therefore multiple ring definitions and fast algorithms are explored. Conversion and standardisation between structure representations present a challenge. Efficient algorithms to determine aromaticity, assign a Kekul? form, and generate tautomers are detailed. Many enzymes are selective and specific to stereochemistry. Methods for the identification, depiction, comparison, and description of stereochemistry are described.

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.

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.

Mycobacterium tuberculosis (Mtb), a pathogenic bacterium, is the causative agent in the vast majority of human tuberculosis (TB) cases. Nearly one-third of the world’s population has been affected by TB and annually two million deaths result from the disease. Because of the high cost of medication for a long term treatment with multiple drugs and the increase of multidrug-resistant Mtb strains, faster-acting drugs and more effective vaccines are urgently demanded. Several metabolic pathways of Mtb are attractive for identifying novel drug targets against TB. Hence, a high quality genome-scale metabolic network of Mtb (HQMtb) was reconstructed to investigate its whole metabolism and explore for new drug targets. The HQMtb metabolic network was constructed using an unbiased approach by extracting gene annotation information from various databases and consolidating the data with information from literature. The HQMtb consists of 686 genes, 607 intracellular reactions, 734 metabolites and 471 E.C. numbers, 27 of which are incomplete. The HQMtb was compared with two recently published Mtb metabolic models, GSMN-TB by Beste et al. and iNJ661 model by Jamshidi and Palsson. Due to the different reconstruction methods used, the three models have different characteristics. The 68 new genes and 80 new E.C. numbers were found only in the HQMtb and resulting in approximately 52 new metabolic reactions located in various metabolic pathways, for example biosynthesis of steroid, fatty acid metabolism, and TCA cycle. Through a comparison of HQMtb with a previously published human metabolic network (EHMN) in terms of protein signatures, 42 Mtb metabolic genes were proposed as new drug targets based on two criteria: (a) their protein functional sites do not match with any human protein functional sites; (b) they are essential genes. Interestingly, 13 of them are found in a list of current validated drug targets. Among all proposed drug targets, Rv0189c, Rv3001c and Rv3607c are of interest to be tested in the laboratory because they were also proposed as drug target candidates from two research groups using different methods.

Genome-scale ordered RNA structure (GORS) was first identified in 2004. It refers to the presence of secondary structure throughout the length of the RNA genomes of certain genera of RNA virus families, as predicted by bioinformatic analysis. It was also observed that the viruses containing GORS were able to establish persistent infections in their natural hosts, raising the possibility that the presence of GORS could play a role in viral avoidance of the innate immune system. This thesis describes the first study of GORS and its possible role in persistence. Two GORS viruses have been studied, equine rhinitis A virus (ERAV) and murine norovirus (MNV). A 55% seroprevalence of ERAV has been determined in a cohort of Scottish horses indicating a wide exposure to the virus. Equine faecal samples were screened for ERAV by PCR with the intention of identifying a virus, possibly from a persistently infected animal, which would not have undergone any cell culture adaptations as laboratory strains have. Newly identified viruses would then be sequenced, their secondary structures predicted and further studies carried out. Unfortunately, none of the 50 faecal samples screened were positive and clinical isolates of ERAV provided by the Animal Health Trust were sequenced but were identical to laboratory strains, so the study then focussed on MNV. Prevalence of MNV in laboratory mice was determined by PCR of faecal samples to be 67%. MNV was also discovered in the faeces of a pet shop mouse and a wild wood mouse (Apodemus sylvaticus). The complete genomes of 4 laboratory mouse MNVs, the pet shop mouse and wood mouse MNVs were sequenced. Phylogenetic analysis showed the wood mouse MNV had a p distance of 23% from other MNVs, although the laboratory mice and pet shop mouse were closely related to other MNVs. Structural analysis of the genomes of 6 sequenced MNVs, including the wood mouse virus, showed all were GORS viruses. A laboratory strain of MNV, MNV-3, was serially passaged in RAW 264.7 cells to test the hypothesis that in an animal with an intact immune system, there is a pressure for GORS viruses to maintain their genomic RNA structure as a means of immune avoidance, and that cell culture adaptation would attenuate the degree of secondary structure. The complete genome of passage 33 was sequenced, which revealed 7 base mutations, a mutation rate of 0.1 %, which was not considered significant enough to have affected the degree of secondary structure. In order to assess if structured and unstructured RNA behaved differently in cells, replication deficient RNA transcripts were made from the infectious clones of a panel of GORS and non-GORS viruses. These transcripts were electroporated into cells and their rate of decay measured, but there was no difference between the GORS and non-GORS transcripts. The full length and 4 kilobase transcripts were transfected into NIH3T3 cells and the degree of interferon-β induction measured by quantitative PCR and a luciferase reporter assay. The IFN-β response differed across the panel of viruses, and although none of the GORS viruses induced strongly, the non-GORS viruses were variable in their ability to induce an IFN-β response, some inducing strongly, other not at all. This result indicates that during exposure of viral genomes in the cytoplasm during infection, GORS-virus RNAs are unlikely to induce an interferon response, possibly contributing to their ability to persist. It is unclear why some non-GORS-viruses failed to induce IFN and there are likely to be other contributory factors.

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.

This study proposes simulations which present optimized methods for producing fatty acids, fatty alcohols and alkanes using Zymomonas mobilis bacterium by the energy efficient β-oxidation reversal pathway, an eco-friendly alternative to the present petroleum based processes. Zymomonas has advantages of higher carbon intake, higher ethanol tolerance and higher ethanol production efficiency than other organisms. I have improved an earlier Zymomonas genome scale model and used Constraint Based Reconstruction and Analysis (COBRA), a linear optimization based computational tool in Matlab, and to perform flux balance analysis (FBA) based simulations. FBA accounts for formation, consumption, accumulation and removal rate or flux of each metabolite. The results present solution spaces of cell growth rate and product formation rate, which trend with products and their carbon chain length. I have analyzed these solution space trends gaining insight into the Zymomonas’ metabolism, enabling efficient product formation and opening a way for future improvement.

We propose a new method to integrate genome-scale metabolic models into biogeochemical reaction modeling. This method predicts rates of microbial metabolisms by combining flux balance analysis (FBA) with microbial rate laws. We applied this new hybrid method to methanogenesis by Methanosarcina barkeri. Our results show that the new method predicts well the progress of acetoclastic, methanol, and diauxic metabolism by M. barkeri. The hybrid method represents an improvement over dynamic FBA. We validated genome-scale metabolic models of Methanosarcina barkeri, Methanosarcina acetivorans, Geobacter metallireducens, Shewanella oneidensis, Shewanella putrefaciens and Shewanella sp. MR4 for application to biogeochemical modeling. FBA was used to predict the response of cell metabolism, and ATP and biomass yield. Our analysis provides improvements to these models for the purpose of applications to natural environments. / 2019-07-28

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Tesis por compendio / [ES] Saccharomyces cerevisiae, además de ser un importante organismo modelo en biología, es indiscutiblemente la especie de levadura más utilizada en procesos fermentativos industriales, incluyendo el sector enológico. Su capacidad de fermentar en concentraciones elevadas de azúcares, tolerar concentraciones altas de etanol y soportar la adición de sulfitos, son algunos de los factores que explican su éxito en fermentaciones vínicas. El metabolismo fermentativo de S. cerevisiae en condiciones enológicas se conoce bien gracias a una amplia bibliografía científica. En cambio, aún se sabe poco sobre el metabolismo de las especies de Saccharomyces criotolerantes, S. uvarum y S. kudriavzevii, quienes han suscitado recientemente el interés del sector vitivinícola por sus buenas propiedades fermentativas a bajas temperaturas, tales como la producción de vinos con mayor contenido en glicerol y alta complejidad aromática, llegando a veces a reducir su contenido en etanol. En este contexto, esta tesis pretende ampliar nuestros conocimientos sobre el metabolismo fermentativo de S. uvarum y S. kudriavzevii en condiciones enológicas, profundizando en el entendimiento de las diferencias existentes con el de S. cerevisiae, así como entre cepas de S. cerevisiae de distintos orígenes. Para ello, hemos utilizado varias técnicas ómicas para analizar la dinámica de los metabolomas (intra- y extracelulares) y/o transcriptomas de cepas representativas de S. cerevisiae, S. uvarum y S. kudriavzevii a alta (25 °C) y baja (12 °C) temperatura de fermentación. También, hemos desarrollado un modelo metabólico a escala de genoma que, junto a un análisis de balance de flujos, es capaz de cuantificar los flujos a través del metabolismo del carbono y del nitrógeno de levaduras en cultivo de tipo batch. Así, el conjunto de estos trabajos nos ha permitido identificar rasgos metabólicos y/o transcriptómicos relevantes para el sector enológico en estas especies. También se aporta nueva información sobre las especificidades de redistribución de flujos en la red metabólica de levaduras del género Saccharomyces acorde a la especie y las fluctuaciones ambientales que ocurren durante una fermentación vínica. / [CAT] Saccharomyces cerevisiae, a més de ser un important organisme model en biologia, és indiscutiblement l'espècie de llevat més utilitzat en processos fermentatius industrials, incloent el sector enològic. La seua capacitat de fermentar grans concentracions de sucres, tolerar concentracions altes d'etanol i suportar l'addició de sulfits, són alguns dels factors que expliquen el seu èxit en fermentacions víniques. D'aquesta manera, el metabolisme fermentatiu de S. cerevisiae en condicions enològiques està ben descrit i es beneficia d'una àmplia bibliografia científica. En canvi, poc se sap encara sobre el metabolisme de les espècies de Saccharomyces criotolerants, S. uvarum i S. kudriavzevii, els qui han recentment suscitat l'interés del sector vitivinícola per les seues bones propietats fermentatives a baixes temperatures, com ara la producció de vins amb major contingut en glicerol, alta complexitat aromàtica i arribant a vegades a reduir el seu contingut en etanol. En aquest context, aquesta tesi pretén ampliar els nostres coneixements sobre el metabolisme fermentatiu de S. uvarum i S. kudriavzevii en condicions enològiques, aprofundint en l'enteniment de les diferències existents amb el de S. cerevisiae, així també com entre ceps de S. cerevisiae de diferents orígens. Per a això, hem utilitzat diverses tècniques omiques per a analitzar la dinàmica dels metabolomes (intra- i extracelul·lars) i/o transcriptomes de ceps representatius de S. cerevisiae, S. uvarum i S. kudriavzevii a alta (25 °C) i baixa (12 °C) temperatures de fermentació. També, hem desenvolupat un model metabòlic a escala del genoma que, al costat d'una anàlisi de balanç de fluxos, és capaç de quantificar els fluxos a través del metabolisme carbonat i nitrogenat de llevats en cultius de tipus batch. Així, el conjunt d'aquests treballs ens ha permés identificar trets metabòlics i/o transcriptómics rellevants per al sector enològic en aquestes espècies. També aporta nova informació sobre les especificitats de redistribució de fluxos en la xarxa metabòlica de llevats del gènere Saccharomyces concorde a l'espècie i les fluctuacions ambientals ocorrent durant una fermentació vínica. / [EN] Saccharomyces cerevisiae, besides being an important model organism in biology, is undoubtedly the most widely used yeast species in industrial fermentation processes, including the winemaking sector. Its ability to ferment at high levels of sugars, tolerate high ethanol concentrations and withstand the addition of sulfites are some of the factors explaining its success in wine fermentation. Accordingly, the fermentative metabolism of S. cerevisiae under oenological conditions is well described and benefits from a large scientific literature. In contrast, little is known about the metabolism of the cryotolerant Saccharomyces species, S. uvarum and S. kudriavzevii, which have recently attracted the interest of the wine industry for their good fermentative properties at low temperatures, such as the production of wines with higher glycerol content, high aromatic complexity and sometimes even reduced ethanol content. In this context, this thesis aims to expand our knowledge on the fermentative metabolism of S. uvarum and S. kudriavzevii under oenological conditions, deepening our understanding of the existing differences with that of S. cerevisiae, as well as between S. cerevisiae strains of different origins. For this purpose, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum and S. kudriavzevii at high (25 °C) and low (12 °C) fermentation temperatures. Also, we have developed a genome-scale metabolic model that, together with a flux balance analysis, is able to quantify fluxes through carbon and nitrogen metabolism of yeast in batch culture. Taken together, this work has allowed us to identify metabolic and/or transcriptomic traits relevant to the oenological sector in these species. It also provides new information on the specificities of flux redistribution in the metabolic network of Saccharomyces yeasts according to the species and environmental fluctuations occurring during wine fermentation. / The present work has been carried out at the Department of Food Biotechnology of the IATA (CSIC). Romain Minebois was funded by a FPI grant (REF: BES-2016-078202) and supported by projects AGL2015-67504-C3-1R and RTI2018-093744-BC31 of the Ministerio de Ciencia e Inovación awarded to Amparo Querol. / Minebois, RCM. (2021). Study of the differences in the fermentative metabolism of S. cerevisiae, S. uvarum and S. kudriavzevii species [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176018 / TESIS / Compendio