Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.

An interesting discovery in biology is that most genes in an organism are dispensable. That means these genes have minor effects on survival of the organism in standard laboratory conditions. One explanation of this discovery is that some genes play important roles in specific conditions and are essential genes under those conditions. E. coli is a model organism, which is widely used. It can adapt to many stress conditions, including temperature, pH, osmotic, antibiotic, etc. Underlying mechanisms and associated genes of each stress condition responses are usually different. In our analysis, we combined protein abundance data and mutant conditional fitness data into E. coli constraint-based metabolic models to study conditionally essential metabolic genes under temperature and pH stress conditions. Flux Balance Analysis was employed as the modeling method to analysis these data. We discovered lists of metabolic genes, which are E. coli dispensable genes, but conditionally essential under some stress conditions. Among these conditionally essential genes, atpA in low pH stress and nhaA in high pH stress found experimental evidences from previous studies. Our study provides new conditionally essential gene candidates for biologists to explore stress condition mechanisms.

L'identification des interactions survenant au niveau moléculaire joue un rôle crucial pour la compréhension du vivant. L'objectif de ce travail a consisté à développer des méthodes permettant de modéliser et de prédire ces interactions pour le métabolisme et la régulation de la transcription. Nous nous sommes basés pour cela sur la modélisation de ces systèmes sous la forme de graphes et d'automates. Nous avons dans un premier temps développé une méthode permettant de tester et de prédire la distribution du flux au sein d'un réseau métabolique en permettant la formulation d'une à plusieurs contraintes. Nous montrons que la prise en compte des données biologiques par cette méthode permet de mieux reproduire certains phénotypes observés in vivo pour notre modèle d'étude du métabolisme énergétique du parasite Trypanosoma brucei. Les résultats obtenus ont ainsi permis de fournir des éléments d'explication pour comprendre la flexibilité du flux de ce métabolisme, qui étaient cohérentes avec les données expérimentales. Dans un second temps, nous nous sommes intéressés à une catégorie particulière d'ARN non codants appelés sRNAs, qui sont impliqués dans la régulation de la réponse cellulaire aux variations environnementales. Nous avons développé une approche permettant de mieux prédire les interactions qu'ils effectuent avec d'autres ARN en nous basant sur une prédiction des interactions, une analyse par enrichissement du contexte biologique de ces cibles, et en développant un système de visualisation spécialement adapté à la manipulation de ces données. Nous avons appliqué notre méthode pour l'étude des sRNAs de la bactérie Escherichia coli. Les prédictions réalisées sont apparues être en accord avec les données expérimentales disponibles, et ont permis de proposer plusieurs nouvelles cibles candidates. / The identification of the interactions occurring at the molecular level is crucial to understand the life process. The aim of this work was to develop methods to model and to predict these interactions for the metabolism and the regulation of transcription. We modeled these systems by graphs and automata.Firstly, we developed a method to test and to predict the flux distribution in a metabolic network, which consider the formulation of several constraints. We showed that this method can better mimic the in vivo phenotype of the energy metabolism of the parasite Trypanosoma brucei. The results enabled to provide a good explanation of the metabolic flux flexibility, which were consistent with the experimental data. Secondly, we have considered a particular class of non-coding RNAs called sRNAs, which are involved in the regulation of the cellular response to environmental changes. We developed an approach to better predict their interactions with other RNAs based on the interaction prediction, an enrichment analysis, and by developing a visualization system adapted to the manipulation of these data. We applied our method to the study of the sRNAs interactions within the bacteria Escherichia coli. The predictions were in agreement with the available experimental data, and helped to propose several new target candidates.

Les anthocyanes sont une famille de polyphénols très répandus chez les végétaux. Chez la vigne, elles sont responsables de la coloration des baies des cépages rouges, et sont impliquées dans les propriétés organoleptiques des vins. Une nutrition azotée faible induit la production des anthocyanes dans les cellules de la pellicule de raisin des cépages rouges via des mécanismes de régulation qui ne sont pas encore totalement élucidés. Dans ce contexte, nous avons étudié les mécanismes moléculaires impliqués dans la réponse de l’accumulation des anthocyanes pour différents niveaux d’apports azotés. Deux matériels biologiques complémentaires ont été utilisés : des suspensions cellulaires de vigne (lignée GT3) et des plants de Cabernet-Sauvignon, cultivés au vignoble.L’augmentation de la synthèse d’anthocyanes en réponse à la diminution de la nutrition azotée a été confirmée dans les baies et les cellules de vigne en culture. Les analyses transcriptomiques globales (génome complet) et ciblées (qPCR) ont mis en lumière des modifications de l’expression génique, notamment de gènes liés au métabolisme des flavonoïdes, en réponse à la nutrition azotée. L’expression de nombreux gènes structuraux impliqués dans la voie de biosynthèse des anthocyanes est induite par une faible nutrition azotée. La variation de l’apport azoté influence également de façon coordonnée l’expression des gènes régulateurs positifs (facteurs de transcription de type MYB) et négatifs (protéine de type Lateral organ Boundary Domain (LBD)) des gènes de la biosynthèse des flavonoïdes chez la Vigne. L’expression de gènes liés à la production d’énergie (NADH, NADPH), est également affectée.En parallèle, une approche intégrative a été développée sur les suspensions cellulaires, en combinant des mesures d’activités enzymatiques, des dosages de métabolites primaires et secondaires, avec un modèle de balance de flux (Flux Balance Analysis, FBA). Les cartes de flux obtenues prédisent que la diminution de l’apport azoté entraîne une augmentation des flux métaboliques dans la voie du shikimate et des phénylpropanoïdes ; ainsi qu’une répression de la majorité des flux dans les différentes voies du métabolisme primaire, à l’exception de la voie des pentoses phosphates, dont le flux est maintenu, et de la voie de synthèse de l’amidon qui est accrue. Les résultats obtenus plaident en faveur d’un lien fort entre synthèse des anthocyanes et statut énergétique (ATP, NADPH) des cellules vigne. / Anthocyanins are polyphenol compounds very abundant in most of the plants. In grapevine, they give color to red berries and they improve red wine quality and increase the organoleptic properties of the wine. Low nitrogen supply stimulates anthocyanin production in berry skin cells of red grape varieties through regulation mechanisms that are far from being fully understood. In this context, we worked on the molecular mechanisms involved in anthocyanin biosynthesis response to nitrogen supply. Two complementary biological materials were used: grapevine cell suspensions (GT3 line) that originate from a teinturier cultivar and produce anthocyanins under normal conditions; and red grape berries of cv. Cabernet-Sauvignon cultivated in a commercial vineyard. Increases of anthocyanins synthesis in response to low nitrogen levels were confirmed in the field-grown berries and the cells suspensions. Both comparative global (microarrays) and targeted (qPCR) transcriptomic analysis showed different regulations on the expression of the genes involved in the secondary (especially the anthocyanin) and nitrogen metabolisms. The expression of most structural genes of the anthocyanin biosynthesis pathway was induced by a low nitrogen supply. Nitrogen controls also the expression of the positive (MYB transcription factors) and negative (Lateral organ Boundary Domain family protein LBD39) regulatory genes of the flavonoid pathway in grapevine. Furthermore, some genes improved in energy production (ATP, NADPH) were affected. In parallel, an integrative approach combining enzymatic activities and primary and secondary metabolites measurements with developing a Flux Balance Analysis (FBA) modeling approach was used on cells suspensions GT3. The flux maps deciphered that low nitrogen increases metabolic fluxes in shikimate and phenylpropanoid pathways and represses the majority metabolic fluxes in different pathways of primary metabolism. The two exceptions included the pentose phosphate pathway, which the flux metabolism was maintained, and the starch synthesis pathway, which was enhanced. The results obtained showed a strong link between anthocyanin synthesis and energy status (ATP, NADPH) in the berry cell suspensions.

The development of human society, which is highly dependent on fossil fuels, is now facing a range of global issues, such as rising energy prices, energy security and climate changes. To successfully tackle the resultant issues, the energy transition from fossil fuels to renewable energy sources, such as solar energy, tide energy, hydroelectric power, geothermal heat and biofuels, is under way. Biodiesel, as an important type of biofuels, has been increasingly produced from vegetable oil or used cooking oil, especially in Europe. Nevertheless, considering the high production cost of biodiesel, there is still much to be done to improve the economics of biodiesel industry. Utilisation of crude glycerol, the main by-product of the biodiesel industry, to produce value-added products appears to be a promising solution. Poly(3-hydroxybutyric acid) (PHB), a biodegradable plastic, can be converted from glycerol by Cupriavidus necator DSM 545 under unbalanced growth conditions, such as nitrogen limitation. One way to enhance the batch production of PHB is to genetically engineer the strain of C. necator, which requires insights of the dynamic impact of extracellular environment on cell phenotypes. Hence in this thesis, we aim to perform metabolic modelling based on experimental measurements to gain a better understanding of the behaviour of the metabolic network of Cupriavidus necator DSM 545 and identify potential bottlenecks of the process. Initially, C. necator DSM 545 is a strain that hardly grows on glycerol, so in a preliminary study, we investigate the process by which the strain was adapted to consume glycerol through serial subcultivation. It is found that the adaptation can be achieved within 15 cell generations over three passages in basal mineral medium, and the acquired phenotype is sufficiently stable upon further passage. The study of metabolism started with the reconstruction of the cell's metabolic network, followed by a thermodynamic analysis to check the feasibility and reversibility of all the biochemical reactions included. Then the static flux balance analysis was extended and applied to analyse the shift of metabolic states during the microbial fermentation in different batch conditions. The resulting patterns of flux distribution reveal the TCA cycle to be the major competitor for PHB synthesis at the ACCoA node. Cells have the potential to enter an efficient PHB-production phase that features minimal TCA/PHB flux split ratio, and the length of the phase can be manipulated by aeration. Although low aeration rate favours optimal flux split ratio, such condition that limits respiration also limits nutrient uptake, leading to low PHB productivity overall. To identify the actual limiting factors of PHB synthesis in the system, we further performed metabolic control analysis based on the calculated flux distributions. The analysis demonstrated how the distribution of the metabolic control can vary widely, depending on the aeration conditions used and the flux split ratios. Glycerolipid pathway, glycolysis, PHB metabolism, as well as the electron transport chain are revealed to be potential engineering targets as they contribute to the great majority of the positive control of PHB flux.

A critical concern in metabolic engineering is the need to balance the demand and supply of redox intermediates. Bioelectrochemical techniques offer a promising method to alleviate redox imbalances during the synthesis of biochemicals. Broadly, these techniques reduce intracellular NAD+ to NADH and therefore manipulate the cell’s redox balance. The cellular response to such redox changes and the additional reducing can be harnessed to produce desired metabolites. In the context of microbial fermentation, these bioelectrochemical techniques can improve product yields and/or productivity. We have developed a method to characterize the role of bioelectrosynthesis in chemical production using the genome-scale metabolic model of E. coli. The results elucidate the role of bioelectrosynthesis and its impact on biomass growth, cellular ATP yields and biochemical production. The results also suggest that strain design strategies can change for fermentation processes that employ microbial electrosynthesis and suggest that dynamic operating strategies lead to maximizing productivity.

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

A critical concern in metabolic engineering is the need to balance the demand and supply of redox intermediates. Bioelectrochemical techniques offer a promising method to alleviate redox imbalances during the synthesis of biochemicals. Broadly, these techniques reduce intracellular NAD+ to NADH and therefore manipulate the cell’s redox balance. The cellular response to such redox changes and the additional reducing can be harnessed to produce desired metabolites. In the context of microbial fermentation, these bioelectrochemical techniques can improve product yields and/or productivity. We have developed a method to characterize the role of bioelectrosynthesis in chemical production using the genome-scale metabolic model of E. coli. The results elucidate the role of bioelectrosynthesis and its impact on biomass growth, cellular ATP yields and biochemical production. The results also suggest that strain design strategies can change for fermentation processes that employ microbial electrosynthesis and suggest that dynamic operating strategies lead to maximizing productivity.

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

With advances in high-throughput technologies and next-generation sequencing, the amount of genomic and proteomic data is dramatically increasing in the post-genomic era. One of the biggest challenges that has arisen is the connection of sequences to their activities and the understanding of their cellular functions and interactions. In this dissertation, I present three different strategies for mining, predicting and visualizing biological functions. In the first part, I present the COMputational Bridges to Experiments (COMBREX) project, which facilitates the functional annotation of microbial proteins by leveraging the power of scientific community. The goal is to bring computational biologists and biochemists together to expand our knowledge. A database-driven web portal has been built to serve as a hub for the community. Predicted annotations will be deposited into the database and the recommendation system will guide biologists to the predictions whose experimental validation will be more beneficial to our knowledge of microbial proteins. In addition, by taking advantage of the rich content, we develop a web service to help community members enrich their genome annotations. In the second part, I focus on identifying the genes for enzyme activities that lack genetic details in the major biological databases. Protein sequences are unknown for about one-third of the characterized enzyme activities listed in the EC system, the so-called orphan enzymes. Our approach considers the similarities between enzyme activities, enabling us to deal with broad types of orphan enzymes in eukaryotes. I apply our framework to human orphan enzymes and show that we can successfully fill the knowledge gaps in the human metabolic network. In the last part, I construct a platform for visually analyzing the eco-system level metabolic network. Most microbes live in a multiple-species environment. The underlying nutrient exchange can be seen as a dynamic eco-system level metabolic network. The complexity of the network poses new visualization challenges. Using the data predicted by Computation Of Microbial Ecosystems in Time and Space (COMETS), I demonstrate that our platform is a powerful tool for investigating the interactions of the microbial community. We apply it to the exploration of a simulated microbial eco-system in the human gut. The result reflects both known knowledge and novel mutualistic interactions, such as the nutrients exchanges between E. coli, C. difficile and L. acidophilus.