Systems biology attempts to understand biological systems through the generation of predictive models that allow the behaviour of the system to be simulated in silico. Metabolic systems biology has in recent years focused upon the reconstruction and constraint-based analysis of genome-scale metabolic networks, which provide computational and mathematical representations of the known metabolic capabilities of a given organism. This thesis initially concerns itself with the development of such metabolic networks, first considering the community-driven development of consensus networks of the metabolic functions of Saccharomyces cerevisiae. This is followed by a consideration of automated approaches to network reconstruction that can be applied to facilitate what has, until recently, been an arduous manual process. The use of such large-scale networks in the generation of dynamic kinetic models is then considered. The development of such models is dependent upon the availability of experimentally determined parameters, from omics approaches such as transcriptomics, proteomics and metabolomics, and from kinetic assays. A discussion of the challenges faced with developing informatics infrastructure to support the acquisition, analysis and dissemination of quantitative proteomics and enzyme kinetics data follows, along with the introduction of novel software approaches to address these issues. The requirement for integrating experimental data with kinetic models is considered, along with approaches to construct, parameterise and simulate kinetic models from the network reconstructions and experimental data discussed previously. Finally, future requirements for metabolic systems biology informatics are considered, in the context of experimental data management, modelling infrastructure, and data integration required to bridge the gap between experimental and modelling approaches.

La recombinaison méiotique joue un double rôle de moteur évolutif en participant à la création d'une diversité génétique soumise à la sélection naturelle et de contrôle dans la fabrication des gamètes lors de la méiose. De plus, en association avec certains mécanismes de réparation, la recombinaison, au travers de la conversion génique biaisée manipule les fréquences alléliques au sein des populations. Les connaissances sur le fonctionnement même de ce processus ont considérablement augmenté ces dernières années faisant découvrir un processus complexe, autant dans son fonctionnement que dans son évolution. Le thème général de la thèse est l'analyse, dans un contexte évolutif, des relations entre les différents rôles et caractéristiques fonctionnelles de la recombinaison. Un modèle de la recombinaison prenant en compte des contraintes liées au contrôle de la méiose et le phénomène d'interférence a permis une comparaison entre espèces au sein des vertébrés et des non-vertébrés de même qu'une comparaison entre sexes. Par ailleurs, nous avons montré l'impact de la localisation spécifique aux sexes des points chauds de recombinaison sur l'évolution du contenu en GC des génomes de plusieurs vertébrés. Finalement, nous proposons un modèle à l'échelle de la génétique des populations, permettant d'analyser l'impact de la recombinaison sur la fréquence de mutations délétères dans les populations humaines. Cette thèse, nous l'espérons, apportera sa pierre à l'étude interdisciplinaire de la recombinaison, à la fois au sein de la biologie et par ses relations au travers de la modélisation avec l'informatique et les mathématiques. / Meiotic recombination plays several critical roles in molecular evolution. First, recombination represents a key step in the production and transmission of gametes during meiosis. Second, recombination facilitates the impact of natural selection by shuffling genomic sequences. Furthermore, the action of certain repair mechanisms during recombination affects the frequencies of alleles in populations via biased gene conversion. Lately, the numerous advancements in the study of recombination have unraveled the complexity of this process regarding both its mechanisms and evolution. The main aim of this thesis is to analyze the relationships between the different causes, characteristics, and effects of recombination from an evolutionary perspective. First, we developed a model based on the control mechanisms of meiosis and inter-crossover interference. We further used this model to compare the recombination strategies in multiple vertebrates and invertebrates, as well as between sexes. Second, we studied the impact of the sex-specific localization of recombination hotspots on the evolution of the GC content for several vertebrates. Last, we built a population genetics model to analyze the impact of recombination on the frequency of deleterious mutation in the human population.

The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.