Le métabolisme des microorganismes est traditionnellement étudié à deux échelles: d’une part, à l’échelle locale, la description des réactions métaboliques et d’autre part, à l’échelle globale, l’étude de la physiologie de la cellule. Malgré des progrès technologiques récents facilitant les études à ces deux échelles, leur exploitation conjointe demeure complexe car le comportement physiologique de la cellule résulte de l’action coordonnée de nombreuses réactions. Les modèles mathématiques globaux du métabolisme ont toutefois récemment permis de relier ces deux échelles. Dans cette thèse, nous explorerons l’utilisation de ces modèles pour compléter la connaissance des réactions à l’aide d’une catégorie particulière de données d’échelle globale : les essentialités de gènes déterminées à partir des phénotypes de croissance de mutants de délétion. Nous nous appuierons pour cela sur la bactérie Acinetobacter baylyi ADP1. Après avoir présenté les développements effectués pour reconstruire un modèle global du métabolisme d’A. baylyi, nous montrerons que la confrontation entre phénotypes observés et phénotypes prédits permet de mettre en évidence des incohérences entre les deux échelles d’observations. Nous montrerons ensuite qu’une interprétation formelle de ces incohérences permet de corriger le modèle et d’améliorer la connaissance du métabolisme. Nous illustrerons ce propos en présentant les corrections que nous avons réalisées à l’aide de phénotypes de mutants d’A. baylyi. Enfin, dans une dernière partie, nous proposerons une méthode permettant d’automatiser la correction des incohérences causées par des erreurs d’association entre gènes et réactions. / Microbial metabolism has traditionally been investigated at two different scales: the finest involves characterizing individually each reaction occurring in the cell; the largest focuses on global cell physiology. While both scales have recently benefited from technological advances, combining them remains, however, especially complex as the global physiological behavior of a cell results from the coordinated action of a large network of reactions. Mathematical modeling approaches have yet shown recently that genome-scale metabolic models could help in linking both scales. In this thesis, we explore the use of such models to expand the knowledge of reactions with a specific type of high-level data: gene essentiality data, assessed using growth phenotypes of deletion mutants. We will use as model organism the bacterium Acinetobacter baylyi ADP1, for which a genome-wide collection of gene deletion mutants has recently been created. Following a presentation of the key steps and developments that have been required to reconstruct a global metabolic model of A. baylyi, we will show that confronting observed and predicted phenotypes highlight inconsistencies between the two scales. We will then show that a formal interpretation of these inconsistencies can guide model corrections and improvements to the knowledge of metabolism. We will illustrate this claim by presenting model corrections triggered by A. baylyi mutant phenotypes. Finally, we will introduce a method that automates the correction of inconsistencies caused by wrong associations between genes and reactions.

Essential genes are those whose presence is imperative for an organism's survival, whereas the functions of non-essential genes may be useful but not critical. Abnormal functionality of essential genes may lead to defects or death at an early stage of life. Knowledge of essential genes is therefore key to understanding development, maintenance of major cellular processes and tissue-specific functions that are crucial for life. Existing experimental techniques for identifying essential genes are accurate, but most of them are time consuming and expensive. Predicting essential genes using computational methods, therefore, would be of great value as they circumvent experimental constraints. Our research is based on the hypothesis that mammalian essential (lethal) and non-essential (viable) genes are distinguishable by various properties. We examined a wide range of features of Mus musculus genes, including sequence, protein-protein interactions, gene expression and function, and found 75 features that were statistically discriminative between lethal and viable genes. These features were used as inputs to create a novel machine learning classifier, allowing the prediction of a mouse gene as lethal or viable with the cross-validation and blind test accuracies of ∼91% and ∼93%, respectively. The prediction results are promising, indicating that our classifier is an effective mammalian essential gene prediction method. We further developed the mouse gene essentiality study by analysing the association between essentiality and gene duplication. Mouse genes were labelled as singletons or duplicates, and their expression patterns over 13 developmental stages were examined. We found that lethal genes originating from duplicates are considerably lower in proportion than singletons. At all developmental stages a significantly higher proportion of singletons and lethal genes are expressed than duplicates and viable genes. Lethal genes were also found to be more ancient than viable genes. In addition, we observed that duplicate pairs with similar patterns of developmental co-expression are more likely to be viable; lethal gene duplicate pairs do not have such a trend. Overall, these results suggest that duplicate genes in mouse are less likely to be essential than singletons. Finally, we investigated the evolutionary age of mouse genes across development to see if the morphological hourglass pattern exists in the mouse. We found that in mouse embryos, genes expressed in early and late stages are evolutionarily younger than those expressed in mid-embryogenesis, thus yielding an hourglass pattern. However, the oldest genes are not expressed at the phylotypic stage stated in prior studies, but instead at an earlier time point - the egg cylinder stage. These results question the application of the hourglass model to mouse development.

Indiana University-Purdue University Indianapolis (IUPUI) / The rapid innovations in biotechnology have led to an exponential growth of data and electronically accessible scientific literature. In this enormous scientific data, knowledge can be exploited, and novel discoveries can be made. In my dissertation, I have focused on the novel molecular mechanism and therapeutic discoveries from big data for complex diseases. It is very evident today that complex diseases have many factors including genetics and environmental effects. The discovery of these factors is challenging and critical in personalized medicine. The increasing cost and time to develop new drugs poses a new challenge in effectively treating complex diseases. In this dissertation, we want to demonstrate that the use of existing data and literature as a potential resource for discovering novel therapies and in repositioning existing drugs. The key to identifying novel knowledge is in integrating information from decades of research across the different scientific disciplines to uncover interactions that are not explicitly stated. This puts critical information at the fingertips of researchers and clinicians who can take advantage of this newly acquired knowledge to make informed decisions. This dissertation utilizes computational biology methods to identify and integrate existing scientific data and literature resources in the discovery of novel molecular targets and drugs that can be repurposed. In chapters 1 of my dissertation, I extensively sifted through scientific literature and identified a novel interaction between Vitamin A and CYP19A1 that could lead to a potential increase in the production of estrogens. Further in chapter 2 by exploring a microarray dataset from an estradiol gene sensitivity study I was able to identify a potential novel anti-estrogenic indication for the commonly used urinary analgesic, phenazopyridine. Both discoveries were experimentally validated in the laboratory. In chapter 3 of my dissertation, through the use of a manually curated corpus and machine learning algorithms, I identified and extracted genes that are essential for cell survival. These results brighten the reality that novel knowledge with potential clinical applications can be discovered from existing data and literature by integrating information across various scientific disciplines.