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.

El objetivo general de la presente tesis doctoral es el desarrollo de herramientas para la obtencion, produccion y aplicacion de dos enzimas glicosidicas: �¿-L-arabinofuranosidasa proveniente del hongo Aspergillus niger (Abf) y �À-D-glucosidasa (Bgl), proveniente de la levadura Candida molischiana. Estas hidrolasas se emplean en la liberacion de azucares en procesos de conversion de biomasa y en la industria alimentaria, pero tambien en la sintesis de aminoglicosidos, glicoconjugados y oligosacaridos, compuestos de alto valor anadido para la industria quimico-farmaceutica. Las enzimas se han expresado en la levadura metilotrofica Pichia pastoris, y se han purificado para caracterizar sus propiedades bioquimicas. Asimismo, se ha comprobado su capacidad para catalizar reacciones de transglicosilacion con alto rendimiento. En relacion a su produccion, se ha establecido y validado un modelo basado en restricciones del metabolismo de Pichia pastoris, evaluando su consistencia mediante analisis de flujos metabolicos posibilistico. El modelo permite estimar la tasa de crecimiento y la distribucion de flujos intracelulares a partir de unos pocos flujos extracelulares medidos experimentalmente. Adicionalmente, el modelo se ha extendido para estimar la productividad de proteina recombinante, y se ha empleado para analizar diferentes condiciones de cultivo de las cepas transgenicas que sobreproducen las enzimas Abf y Bgl. Finalmente, las enzimas se han inmobilizado en organosilicas bimodales de la familia UVM-7. Los biocatalizadores resultantes se han caracterizado bioquimica y fisico-quimicamente y se han evaluado en diferentes aplicaciones de interes biotecnologico. / Tortajada Serra, M. (2012). Process development for the obtention and use of recombinant glycosidases: expression, modelling and immobilisation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16800

Le cadre général de cette thèse est la question de la relation génotype-phénotype, abordée à travers l'analyse du phénomène d'hétérosis chez la levure, dans une approche associant biologie, mathématiques et statistiques. Antérieurement à ce travail, un très gros jeu de données hétérogènes, correspondant à différents niveaux d'organisation (protéomique, caractères de fermentation et traits d'histoire de vie), avait été recueilli sur un dispositif demi-diallèle entre 11 souches appartenant à deux espèces. Ce type de données est idéalement adapté pour la modélisation multi-échelle et pour tester des modèles de prédiction de la variation de phénotypes intégrés à partir de caractères protéiques et métaboliques (flux), tout en tenant compte des structures de dépendance entre variables et entre observations. J’ai d'abord décomposé, pour chaque caractère, la variance génétique totale en variances des effets additifs, de consanguinité et d'hétérosis, et j’ai montré que la distribution de ces composantes permettait de définir des groupes bien tranchés de protéines dans lesquels se plaçaient la plupart des caractères de fermentation et de traits d'histoire de vie. Au sein de ces groupes, les corrélations entre les variances des effets d'hétérosis et de consanguinité pouvaient être positives, négatives ou nulles, ce qui a constitué la première mise en évidence expérimentale d’un découplage possible entre les deux phénomènes. Le second volet de la thèse a consisté à interfacer les données de protéomique quantitative avec un modèle stœchiométrique du métabolisme carboné central de la levure, en utilisant une approche de modélisation à base de contraintes. M'appuyant sur un algorithme récent, j’ai cherché, dans l'espace des solutions possibles, celle qui minimisait la distance entre le vecteur de flux et le vecteur des abondances observées des protéines. J’ai ainsi pu prédire un ensemble de flux et comparer les patrons de corrélations entre caractères à plusieurs niveaux d'intégration. Les données révèlent deux grandes familles de caractères de fermentation ou de traits d'histoire de vie dont l'interprétation biochimique est cohérente en termes de trade-off, et qui n'avaient pas été mises en évidence à partir des seules données de protéomique quantitative. L'ensemble de mes travaux permet de mieux comprendre l'évolution de la relation entre génotype et phénotype. / The general framework of this thesis is the issue of the genotype-phenotype relationship, through the analysis of the heterosis phenomenon in yeast, in an approach combining biology, mathematics and statistics. Prior to this work, a very large set of heterogeneous data, corresponding to different levels of organization (proteomics, fermentation and life history traits), had been collected on a semi-diallel design involving 11 strains belonging to two species. This type of data is ideally suited for multi-scale modelling and for testing models for predicting the variation of integrated phenotypes from protein and metabolic (flux) traits, taking into account dependence patterns between variables and between observations. I first decomposed, for each trait, the total genetic variance into variances of additive, inbreeding and heterosis effects, and showed that the distribution of these components made it possible to define well-defined groups of proteins in which most of the characters of fermentation and life history traits took place. Within these groups, the correlations between the variances of heterosis and inbreeding effects could be positive, negative or null, which was the first experimental demonstration of a possible decoupling between the two phenomena. The second part of the thesis consisted of interfacing quantitative proteomic data with the yeast genome-scale metabolic model using a constraint-based modelling approach. Using a recent algorithm, I looked, in the space of possible solutions, for the one that minimized the distance between the flux vector and the vector of the observed abundances of proteins. I was able to predict unobserved fluxes, and to compare correlation patterns at different integration levels. Data allowed to distinguish between two major types of fermentation or life history traits whose biochemical interpretation is consistent in terms of trade-off, and which had not been highlighted from quantitative proteomic data alone. Altogether, my thesis work allows a better understanding of the evolution of the genotype-phenotype map.

This thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells. The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed in detail, network-based pathways analysis (NPA), metabolic flux analysis (MFA), and flux balance analysis (FBA). Four types of metabolic pathways are also compared to clarify the subtle differences among them. The second part is devoted to interval methods for constraint-based models. The first contribution is an interval approach to traditional MFA, particularly useful to estimate the metabolic fluxes under data scarcity (FS-MFA). These estimates provide insight on the internal state of cells, which determines the behaviour they exhibit at given conditions. The second contribution is a procedure for monitoring the metabolic fluxes during a cultivation process that uses FS-MFA to handle uncertainty. The third part of the document addresses the use of possibility theory. The main contribution is a possibilistic framework to (a) evaluate model and measurements consistency, and (b) perform flux estimations (Poss-MFA). It combines flexibility on the assumptions and computational efficiency. Poss-MFA is also applied to monitoring fluxes and metabolite concentrations during a cultivation, information of great use for fault-detection and control of industrial processes. Afterwards, the FBA problem is addressed. / Llaneras Estrada, F. (2011). Interval and Possibilistic Methods for Constraint-Based Metabolic Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10528

À l’issue de ce travail de thèse, je propose une approche basée sur le formalisme des modèles à base de contraintes, pour étudier la dynamique d’un système métabolique. En associant l’échantillonnage de l’espace des solutions avec l’utilisation d’une contrainte de « faisabilité » entre les périodes de temps considérées, cette approche permet de modéliser la dynamique d’un système métabolique en prenant en compte la variabilité des mesures expérimentales. La contrainte de faisabilité entre les périodes permet de garantir que chaque « trajectoire solution » correspond à une succession de cartes de flux qui conduit à des cinétiques de concentrations cohérentes avec les mesures expérimentales. Les populations de trajectoires solutions générées autorisent différents types d’analyses. D’une part, les répartitions de flux prédites peuvent être utilisées afin d’estimer les répartitions de flux les plus plausibles au sein du réseau étudié. D’autre part, la distribution des concentrations prédites permet d’évaluer le modèle utilisé pour étudier le réseau métabolique. Le fait que cette approche soit basée sur le formalisme de la modélisation à base de contraintes permet, moyennant l’utilisation de l’hypothèse d’état stationnaire du système, d’étudier des réseaux métaboliques de taille relativement grande, et d’utiliser des données expérimentales qui sont aisément mesurables, par exemple les concentrations en biomasse et en métabolites extracellulaires. Cette approche par « trajectoires solutions » a été utilisée afin d’étudier la dynamique du métabolisme de Corynebacterium glutamicum, lorsqu’elle est cultivée en condition de limitation en biotine. Les résultats obtenus ont permis d’une part d’attester du fonctionnement de la méthode, et d’autre part de proposer plusieurs hypothèses quant aux phénomènes biologiques qui ont lieu pendant cette condition particulière de croissance. / In this thesis, I propose an approach based on the formalism of constraint-based models to study the dynamics of a metabolic system. By combining the sampling of the solutions space and the use of a "feasibility" constraint between the considered time periods, this approach allows to model the dynamic of a metabolic system taking into account the variability of experimental measurements. The feasibility constraint between time periods ensures that each "solution trajectory" corresponds to a succession of flux maps which leads to some kinetics of concentrations that are consistent with the experimental measurements. The generation of a population of solution trajectories allows several analyses. On the one hand, the predicted flux maps can be used to estimate the most plausible flux within the network studied. On the other hand, the distribution of predicted concentrations enables to assess the model used for studying the metabolic network. The fact that this approach is based on the formalism of constraint-based modeling allows, using the steady-state assumption of the system, to study metabolic networks of relatively large size, and to use experimental data that are easily measurable, such as biomass concentration and extracellular metabolites concentration. This approach by "solution trajectories" has been used to study the dynamics of the metabolism of Corynebacterium glutamicum, when grown under biotin-limited condition. The results allowed, first, to attest the functioning of the method, and second, to propose several hypotheses about biological phenomena that take place during this particular growth condition.