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BACKGROUND:Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them an attractive bacterial target for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of 21 sequenced Shewanella strains.RESULTS:In this work, we updated the model for Shewanella oneidensis MR-1 and constructed metabolic models for three other strains, namely Shewanella sp. MR-4, Shewanella sp. W3-18-1, and Shewanella denitrificans OS217 which span the genus based on the number of genes lost in comparison to MR-1. We also constructed a Shewanella core model that contains the genes shared by all 21 sequenced strains and a few non-conserved genes associated with essential reactions. Model comparisons between the five constructed models were done at two levels - for wildtype strains under different growth conditions and for knockout mutants under the same growth condition. In the first level, growth/no-growth phenotypes were predicted by the models on various carbon sources and electron acceptors. Cluster analysis of these results revealed that the MR-1 model is most similar to the W3-18-1 model, followed by the MR-4 and OS217 models when considering predicted growth phenotypes. However, a cluster analysis done based on metabolic gene content revealed that the MR-4 and W3-18-1 models are the most similar, with the MR-1 and OS217 models being more distinct from these latter two strains. As a second level of comparison, we identified differences in reaction and gene content which give rise to different functional predictions of single and double gene knockout mutants using Comparison of Networks by Gene Alignment (CONGA). Here, we showed how CONGA can be used to find biomass, metabolic, and genetic differences between models.CONCLUSIONS:We developed four strain-specific models and a general core model that can be used to do various in silico studies of Shewanella metabolism. The developed models provide a platform for a systematic investigation of Shewanella metabolism to aid researchers using Shewanella in various biotechnology applications.

In this study, a genome-scale metabolic model was constructed using published genomic and biochemical data. By employing the flux balance analysis (FBA) framework, the model was able to accurately predict ethanol yields on various carbon substrates, secretion profiles, and key characteristics of central metabolism under various growth conditions. A by-product of the model development was the construction of a pathway genome data-base (PGDB) of the yeast, named PichCyc. The PGDB was constructed using Pathway Tools and the database contains 5,841 genes, 1,457 reactions, 994 gene-associated reactions, and 1,475 metabolites. Pathway Tools also facilitated the annotation of an additional 155 genes through the use of Pathway Hole Filler. Together with the constraint-based model, a platform for model-guided metabolic engineering, high-throughput data contextualization, and a public-access online genomic/biochemical database was created.

In this study, a genome-scale metabolic model was constructed using published genomic and biochemical data. By employing the flux balance analysis (FBA) framework, the model was able to accurately predict ethanol yields on various carbon substrates, secretion profiles, and key characteristics of central metabolism under various growth conditions. A by-product of the model development was the construction of a pathway genome data-base (PGDB) of the yeast, named PichCyc. The PGDB was constructed using Pathway Tools and the database contains 5,841 genes, 1,457 reactions, 994 gene-associated reactions, and 1,475 metabolites. Pathway Tools also facilitated the annotation of an additional 155 genes through the use of Pathway Hole Filler. Together with the constraint-based model, a platform for model-guided metabolic engineering, high-throughput data contextualization, and a public-access online genomic/biochemical database was created.

The elucidation of molecular function of proteins encoded by genes is a major challenge in biology today. Genes regulate the amount of proteins (enzymes) needed to catalyse a metabolic reaction. There are several works on either the modelling of gene expression or metabolic network. However, an integrative model of both is not well understood and researched. The integration of both gene expression and metabolic network could increase our understanding of cellular functions and aid in analysing the effects of genes on metabolism. It is now possible to build genome-scale models of cellular processes due to the availability of high-throughput genomic, metabolic and fluxomic data along with thermodynamic information. Integrating biological information at various layers into metabolic models could also improve the robustness of models for in silico analysis. In this study, we provide a software tool for the in silico reconstruction of genome-scale integrative models of gene expression and metabolic network from relevant database(s) and previously existing stoichiometric models with automatic generation of kinetic equations of all reactions involved. To reduce computational complexity, compartmentalisation of the cell as well as enzyme inhibition is assumed to play a negligible role in metabolic function. Obtaining kinetic parameters needed to fully define and characterise kinetic models still remains a challenge in systems biology. Parameters are either not available in literature or unobtainable in the lab. Consequently, there have been numerous methods developed to predict biological behaviour that do not require the use of detailed kinetic parameters as well as techniques for estimation of parameter values based on experimental data. We present an algorithm for estimating kinetic parameters which uses fluxes and metabolites to constrain values. Our results show that our genetic algorithm is able to find parameters that fit a given data set and predict new biological states without having to re-estimate kinetic parameters.

Made available in DSpace on 2015-05-14T13:21:11Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2503826 bytes, checksum: efc2f2118eacd06235948cf7c3c38d1f (MD5) Previous issue date: 2011-03-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this work we have developed an automated methodology for the analysis of metals in gasoline and naphtha in the form of microemulsions using a flow-batch analyzer with piston propulsion for the preparation of microemulsions and quantification of analytes by GF AAS. Three studies were conducted, the first concerning the study of optimal composition of microemulsions made through the construction of phase diagrams for gasoline and naphtha. The second study sought to improve all operational parameters of the automatic system for the production of solutions and also in relation to their coupling to the instrument for analysis. Finally it was done to optimize the operating parameters of the GF AAS for the quantification of metals which have been quantized in two ways, the first quantification by a procedure manual preparation of microemulsions and the second by the same automatic preparation. The two results were then compared by paired t test at a 95% level of confidence, no significant differences between the two relevant procedures. Furthermore, recovery tests performed showed acceptable accuracy for the two methods investigated, with average recoveries in the ranges from 88 to 93% for the automatic method and between 86 to 92% without significant differences in both a 95% level of confidence. The characteristic masses obtained were 0.36pg for Cu, 0.29pg for Cr and 0.87pg for Pb, with limits of detection and quantification respectively of 0.073 and 0.22 for Cu, 0.024 and 0.72 for Cr and 0.115 and 0.346 for Pb. / Neste trabalho foi desenvolvida uma metodologia automática para a análise de metais em gasolina e nafta na forma de microemulsões utilizando um analisador em fluxo-batelada com propulsão a pistão para a preparação das soluções e posterior quantificação dos analitos por GF AAS. Três estudos foram realizados, sendo o primeiro referente ao estudo da composição ótima das microemulsões feito através da construção de diagramas de fases para a gasolina e para a nafta. O segundo estudo tratou de aperfeiçoar todos os parâmetros operacionais do sistema automático para a produção das soluções e também em relação ao seu acoplamento ao instrumento de análise. Por fim foi realizado a otimização dos parâmetros operacionais do GF AAS para a quantificação dos metais, que foram quantizados de dois modos, sendo a primeira quantificação através de um procedimento de preparação manual das microemulsões e a segunda pela preparação automática das mesmas. Os dois resultados obtidos foram então comparados através do teste t pareado a um nível de 95% de confiança, sem diferenças estatísticas relevantes entre os dois procedimentos. Além disso, testes de recuperação realizados indicaram precisão satisfatória para as duas metodologias investigadas, com recuperações médias nas faixas de 88 a 93% para o método automático e entre 86 a 92% ambos sem diferenças significativas a um nível de 95% de confiança. As massas características obtidas foram de 0,36pg para Cu, 0,29pg para Cr e 0,87pg para Pb, com limites de detecção e quantificação respectivamente de 0,073 e 0,22 para Cu, 0,024 e 0,72 para Cr e 0,115 e 0,346 para Pb.

Cyanobakterien sind einzellige, phototrophe Mikroorganismen, die aufgrund ihrer Fähigkeit, Sonnenenergie und Kohlenstoffdioxid für das Zellwachstum zu nutzen, ideale Modellorganismen für ein besseres Verständnis des phototrophen Stoffwechsels darstellen. Sie werden auch zunehmend als potenzieller Wirtsorganismus für die Synthese von wertvollen Chemikalien und verschiedenen Biokraftstoffen wahrgenommen. Um diese Vorteile in vollem Umfang zu nutzen, bietet die genomskalige Rekonstruktion von Mikroorganismen ein weitgehendes Verständnis über die metabolischen Umwandlungen, die während des phototrophen Wachstums stattfinden. In dieser Arbeit wurden detaillierte Rekonstruktionen des metabolischen Netzwerkes der Cyanobakterien Synechocystis sp. PCC 6803 (Synechocystis) und Synechococcus elongatus PCC 7942 erstellt und analysiert. Darüber hinaus wurden beim Netzwerk von Synechocystis unklare Reaktionsschritte experimentell validiert und die funktionellen Konsequenzen von abweichenden Stoffwechseltopologien untersucht. Dabei umfasst das Modell neuartige Ergebnisse in Bezug auf den cyanobakteriellen TCA-Zyklus, einen angeblich vorhandenen Glyoxylatzyklus und die Rolle der Photorespiration beim Zellwachstum, die mithilfe der Flussbilanzanalyse (FBA) systematisch auch in Bezug auf den täglichen Hell-Dunkel-Zyklus des cyanobakteriellen Stoffwechsels gewonnen wurden. Ein weiterer Aspekt dieser Arbeit liegt im Zugewinn des allgemeinen Verständnisses über die genomische Diversität innerhalb unterschiedlicher Cyanobakterienstämme und speziell innerhalb des cyanobakteriellen Stoffwechsels. Dazu wurden die Genome mehrerer phototropher Cyanobaktierenstämme untereinander verglichen und besonders Unterschiede und Gemeinsamkeiten auf Ebene des Stoffwechsels hervorgehoben. Zum Abschluss dieser Arbeit wurde unter Zuhilfenahme der FBA die Synthese von neun unterschiedlichen Biokraftstoffen im metabolischen Netzwerk von Synechocystis, analysiert. / Cyanobacteria are unicellular, phototrophic microorganisms. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, they represent ideal model organisms to better understand phototrophic metabolism. Moreover they are gaining increasing attention as a potential host organism for the synthesis of valuable chemicals and various biofuels. To fully harness this potential of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions that take place during phototrophic growth, such as that provided by genome-scale reconstructions of microbial organisms. In this work, detailed metabolic network of two cyanobacteria, Synechocystis sp. PCC 6803 (Synechocystis) and Synechococcus elongatus PCC 7942, were reconstructed and analyzed. In addition uncertain reaction steps in the network of Synechocystis were experimentally validated and the functional consequences of aberrant metabolic topologies were examined. The model integrates novel results regarding the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth, which were systematically obtained studying the diurnal light/dark cycles of cyanobacterial metabolism using flux balance analysis (FBA). Another aspect of this work focuses on gaining general understanding of the genomic diversity within different cyanobacterial species and specifically within cyanobacterial metabolism. For this purpose, the genomes of several phototrophic cyanobacterial strains were compared and in particular differences and similarities at the level of metabolism were highlighted. In conclusion I reflected on the biotechnological relevance of metabolic network reconstruction by analyzing the synthesis of nine different biofuels in the metabolic network of Synechocystis using FBA.

Au cours des années, de nombreuses données ont été accumulées concernant le rôle et la régulation des facteurs de transcription MYB30 et MYB96 lors des réponses de défense de la plante Arabidopsis thaliana à l'attaque de bactéries pathogènes. Mon travail de thèse a consisté en la mise en place de méthodes de modélisation mathématique afin d'étudier l'effet de ces facteurs de transcription sur le métabolisme de la plante durant l'infection. Pour cela, j'ai développé des méthodes hybrides capables de combiner l'analyse de réseaux de régulation et du métabolisme. Ces études ont pu mettre en évidence l'importance de MYB96 qui semble réguler de nombreux gènes impliqués dans la biosynthèse d'acides gras à très longue chaîne et de leurs dérivés. / Over the years, a lot of data has been accumulated concerning the role and regulation of MYB30 and MYB96 transcription factors during the defence responses of the plant Arabidopsis thaliana in response to pathogenic bacteria. My PhD project consisted in using mathematical modelling methods to study the role of these transcription factors on plant metabolism during infection. I developed hybrid methods capable of combining analyses of regulatory and metabolic networks. These studies showed the importance of MYB96 which seems to positively regulate many genes involved in the biosynthesis of very long chain fatty acids and their derivatives.

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.

Manganese-deficient Streptococcus sanguinis mutants exhibit a dramatic decrease in virulence for infective endocarditis and in aerobic growth in manganese-limited media. Loss of activity of a manganese-dependent, oxygen-dependent ribonucleotide reductase (RNR) could explain the decrease in virulence. When the genes encoding this RNR are deleted, there is no growth of the mutant in aerobic broth culture or in an animal model. Testing the contribution of the aerobic RNR to the phenotype of a manganese transporter mutant, a heterologous class II RNR from Lactobacillus leichmannii called NrdJ that requires B12 rather than manganese as a cofactor was previously introduced into an RNR mutant of S. sanguinis. Aerobic growth was only partially restored. Currently, we sought to improve NrdJ-dependent growth by (i) amending the medium to increase cellular levels of B12; (ii) characterizing a spontaneous mutant of the NrdJ-complemented strain with improved aerobic growth; and (iii) altering this strain through further genetic manipulation.