The mammalian E2F family of transcription factors plays a crucial role in the regulation of cellular proliferation, apoptosis and differentiation. E2F7 and E2F8 are the most recently identified family members and have unusual features that distinguish them from other members in the E2F family, including two distinct DNA-binding domains that bind to DNA in a DP-independent manner. E2F7 and E2F8 have been shown to be transcriptional repressors. However, the mechanism by which E2F7 represses E2F responsive gene expression remains to be elucidated. The results presented here provide the first insight into the E2F7-mediated transcriptional mechanism. E2F7 was shown to contain a CtBP binding motif and form a complex with CtBP in both HeLa and MCF7 cells. An E2F7 deletion mutant lacking the CtBP binding motif was unable to form a complex with CtBP and repress the transcription of E2F target genes in luciferase assays, suggesting that this motif is essential for E2F7-dependent repression. Furthermore, the E2F7-CtBP complex was shown to be stable under different types of damage, such as following etoposide and UV treatment, and under different cell redox states. Interestingly, however, E2F7 was unable to repress the transcriptional activity of E2F target genes following treatment with the CtBP substrate MTOB. Moreover, E2F7 endogenous immunoprecipitations showed that E2F7 forms a complex with the chromatin-modifying enzymes HDAC1, HDAC2 and LSD1 and the co-repressor CoREST. Interestingly, via chromatin immunoprecipitations, E2F7 was shown to recruit these co-repressors to a subset of E2F-responsive promoters where they affect the activity of these promoters in a target gene-specific manner. Furthermore, results presented here suggest that CtBP could play a dual role in E2F7 function, not only being involved in E2F7-mediated repression but also in the repression of E2F7 itself as siRNA mediated CtBP depletion was shown to cause an upregulation of E2F7 protein levels. These results implicate a repertoire of co-repressors in a target gene-specific E2F7 repression mechanism, and as such define a new level of complexity in cell cycle control.

Haematopoietic stem cells (HSC) are strictly regulated by intrinsic regulators and extrinsic signals from the microenvironment. Nov (CCN3), a matricellular protein of the CCN family, has been reported as a suppressor gene in solid tumours and chronic myeloid leukaemia (CML). Recent study identified Nov as a positive regulator in human cord blood CD34+ stem cells. However, the functions of Nov in haematopoiesis and adult HSC remain largely unknown.

Two-pore channels (TPCs, gene name Tpcn) have recently been identified as endolysosomal cation channels modulated by the potent calcium (Ca2+) releasing messenger nicotinic acid adenine dinucleotide phosphate (NAADP). Gene knockout (KO) and RNA knockdown studies have implicated TPCs in fundamental cellular processes, including secretion, of insulin in pancreatic islets, and differentiation, of skeletal myoblasts and osteoclasts. Investigations of Tpcn1 and Tpcn2 mRNA expression have indicated widespread tissue distribution, but a lack of suitable antibodies has impeded study of the endogenous proteins. In this study, an anti-TPC1 antibody was purified from immune sera and used in immunoblotting investigations to demonstrate TPC1 protein expression in a wide range of mouse tissues, with highest expression levels observed in kidney, liver and adipose tissue. Endogenous mouse TPC1 was demonstrated to be glycosylated, with apparent differences in the extent of glycosylation in different tissues based on the indicated molecular weight before and after treatment with a deglycosylating enzyme, which may have implications for the functional regulation of channel activity. Given the increasing prevalence of type 2 diabetes and obesity, an understanding of the molecular basis of glucose homeostasis and adipose tissue formation and function is an important scientific goal. Tpcn KO mice have been developed; in both Tpcn1 KOs and Tpcn2 KOs, impaired pancreatic β-cell Ca2+ signalling and reduced insulin secretion from the whole pancreas were demonstrated. However, the whole-animal phenotype has not been extensively researched. In this study, intraperitoneal glucose tolerance tests were conducted in Tpcn KO mice. These indicated that glucose homeostasis was not significantly affected in Tpcn2 KOs or Tpcn1/2 double KOs (DKOs), and only mildly impaired in Tpcn1 KOs, despite the defects previously observed at the cellular and tissue level. In addition, body composition was investigated in Tpcn1 KO, Tpcn2 KO and Tpcn1/2 DKO animals using magnetic resonance spectroscopy and time domain-nuclear magnetic resonance. Single Tpcn KOs were found to have lower adipose tissue levels as a percentage of body composition, while Tpcn1/2 DKOs were shown to have increased bodyweight but normal body composition. To investigate potential roles for TPCs in adipose tissue formation, Tpcn expression during adipogenesis was investigated using an in vitro multipotent mesenchymal stem cell line model of adipogenic differentiation. Tpcn2 mRNA levels were demonstrated by quantitative PCR to be transiently increased during the early stages of adipogenic differentiation, and cyclic AMP (cAMP) was identified as the factor that induced this upregulation. Lentiviruses were developed to express fluorescently-tagged TPCs, and overexpression of TPC2 was demonstrated to partially overcome the requirement for the cAMP-inducing agent in the medium used for the induction of adipogenesis. Collectively, these data suggest that TPCs may play a role in the formation and/or function of adipose tissue.

Tomatoes are the fourth most valuable commodity in agriculture after rice, wheat and soybeans globally with 151 million tonnes of fruit being produced in 2012. The tomato fruit is also a model system for fleshy fruit development. During ethylene-regulated fruit ripening there are complex changes in fruit chemical composition due to degradation and synthesis of a number of soluble and volatile metabolites. Ultimately, these changes control the composition of the ripe fruit and dictate its flavour and texture. It is known that ripening can proceed when mature green fruit are removed from the plant (and indeed this is standard commercial practice) but the extent to which metabolic changes are sustained when fruit are ripened in this way has yet to be established. A modelling approach such as constraints-based modelling can provide system-level insights into the workings of the complex tomato metabolic network during ripening. The first aim of this thesis was therefore to construct a genome-scale metabolic network model for tomato and to use this model to explore metabolic network flux distributions during the transitions between the stages of fruit ripening. The flux distributions predicted provided insight into the production and usage of energy and reductants, into routes for climacteric CO₂ release, and the metabolic routes underlying metabolite conversions during ripening. The second aim of this thesis was to use the model to explore metabolic engineering strategies for increased production of lycopene in tomato fruit. The model predictions showed that rearrangement of dominant metabolic fluxes were required to cope with the increased demand for reductants at high lycopene accumulation, which came at a cost of a lower accumulation of other secondary metabolites. Overall the thesis provides an approach to connect underlying metabolic mechanisms to the known metabolic processes that happen during ripening.

This thesis focuses on multilocus methods designed to detect single nucleotide polymorphisms (SNPs) that are associated with disease using case-control data. I study multilocus methods that allow for interaction in the regression model because epistasis is thought to be pervasive in the etiology of common human diseases. In contrast, the single-SNP models widely used in genome wide association studies (GWAS) are thought to oversimplify the underlying biology. I consider both pairwise interactions between individual SNPs and modular interactions between sets of biologically similar SNPs. Modular epistasis may be more representative of disease processes and its incorporation into regression analyses yields more parsimonious models. My methodological work focuses on strategies to increase power to detect susceptibility SNPs in the presence of genetic interaction. I emphasize the effect of gene-gene independence constraints and explore methods to relax them. I review several existing methods for interaction analyses and present their first empirical evaluation in a GWAS setting. I introduce the innovative retrospective Tukey score test (RTS) that investigates modular epistasis. Simulation studies suggest it offers a more powerful alternative to existing methods. I present diverse applications of these methods, using data from a multi-stage GWAS on prostate cancer (PRCA). My applied work is designed to generate hypotheses about the functionality of established susceptibility regions for PRCA by identifying SNPs that affect disease risk through interactions with them. Comparison of results across methods illustrates the impact of incorporating different forms of epistasis on inference about disease association. The top findings from these analyses are well supported by molecular studies. The results unite several susceptibility regions through overlapping biological pathways known to be disrupted in PRCA, motivating replication study.

A recent application of point processes has emerged from the electronic trading of financial assets. Many securities are now traded on purely electronic exchanges where demand and supply are aggregated in limit order books. Buy and sell trades in the asset as well as quote additions and cancellations can then be interpreted as events that not only determine the shape of the order book, but also define point processes that exhibit a rich internal structure. A large class of such point processes are those driven by a diffusive intensity process. A flexible choice with favourable analytic properties is a Cox-Ingersoll-Ross (CIR) diffusion. We adopt a Bayesian perspective on the statistical inference for these doubly stochastic processes, and focus on filtering the latent intensity process. We derive analytic results for the moment generating function of its posterior distribution. This is achieved by solving a partial differential equation for a linearised version of the filtering equation. We also establish an efficient and simple numerical evaluation of the posterior mean and variance of the intensity process. This relies on extending an equivalence result between a point process with CIR-intensity and a partially observed population process. We apply these results to empirical datasets from foreign exchange trading. One objective is to assess whether a CIR-driven point process is a satisfactory model for the variations in trading activity. This is answered in the negative, as sudden bursts of activity impair the fit of any diffusive intensity model. Controlling for such spikes, we conclude with a discussion of the stochastic control of a market making strategy when the only information available are the times of buy and sell trades.

E2F-1 has a central role in cell cycle orchestration, and its activity is tightly regulated. One of the ways E2F-1 activity is controlled is by direct modification by post translational modifications such as acetylation, ubiquitination and phosphorylation. Here it was demonstrated that E2F-1 is targeted by two novel modifications, namely methylation by Set7/9 and NEDDylation, both within the DNA binding and heterodimerisation domain of the protein. NEDDylation and methylation of E2F-1 both decrease the stability and diminish the transcriptional activity of E2F-1. Lysine residues in E2F-1 involved in NEDDylation are also targeted by methylation, allowing the potential for interplay between these modifications. Methylation of E2F-1 was demonstrated to be a prerequisite for its NEDDylation and the multi-domain protein UHRF1 implicated in mediating this effect. The results define a new level of control on E2F-1 and suggest a protein code with pleiotropic effects involved in E2F-1 regulation.

The Chinese, Indian and Myanmar governments share the borderlands in the corners of their respective territories where East, South and Southeast Asia meet. In this region of common concern the capacities of these three systems of post-colonial government are regulated so as to prevent excessive political conflict and discourage territorial fragmentation. My research focus is how the governments seek to exert spatial control in areas occupied by the closely-related Jingpo, Singpho and Jinghpaw peoples. As part of their efforts to shape interactions with the central governments, local elites among these peoples have defended and expanded elements of their Jingpo, Singpho and Jinghpaw cultures, particularly their annual Manau festivals. Seeking a way to analyse the relationship between governments and those they govern I draw on the illustrative potential of these large-scale events. It is the symbolic politics of these festivals that suggest an argument about spatial control that refines the state-repelling “Zomia” model proposed by van Schendel (2002) and Scott (2009a). I argue that nodes of control are sites where the governments concentrate power in order to manage their geopolitical ambitions. These nodes succeed when they encourage the acquiescence of local economic and cultural elites. By opening up opportunities for such collaboration, the nodes buttress the strategic links—cultural, political, economic, transportation and communications—that are the main interests of all central governments. It is, moreover, the intrinsic limitation of government ambitions, and their willingness to allow creative ambiguities, that suggests the direction in which ideas about spatial control at the intersection of China, India and Burma can be re conceived.

Emerging many-core computer architectures provide an incentive for computational methods to exhibit specific types of parallelism. Our ability to perform inference in Bayesian statistics is often dependent upon our ability to approximate expectations of functions of random variables, for which Monte Carlo methodology provides a general purpose solution using a computer. This thesis is primarily concerned with exploring the gains that can be obtained by using many-core architectures to accelerate existing population-based Monte Carlo algorithms, as well as providing a novel general framework that can be used to devise new population-based methods. Monte Carlo algorithms are often concerned with sampling random variables taking values in X whose density is known up to a normalizing constant. Population-based methods typically make use of collections of interacting auxiliary random variables, each of which is in X, in specifying an algorithm. Such methods are good candidates for parallel implementation when the collection of samples can be generated in parallel and their interaction steps are either parallelizable or negligible in cost. The first contribution of this thesis is in demonstrating the potential speedups that can be obtained for two common population-based methods, population-based Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC). The second contribution of this thesis is in the derivation of a hierarchical family of sparsity-inducing priors in regression and classification settings. Here, auxiliary variables make possible the implementation of a fast algorithm for finding local modes of the posterior density. SMC, accelerated on a many-core architecture, is then used to perform inference for a range of prior specifications to gain an understanding of sparse association signal in the context of genome-wide association studies. The third contribution is in the use of a new perspective on reversible MCMC kernels that allows for the construction of novel population-based methods. These methods differ from most existing methods in that one can make the resulting kernels define a Markov chain on X. A further development is that one can define kernels in which the number of auxiliary variables is given a distribution conditional on the values of the auxiliary variables obtained so far. This is perhaps the most important methodological contribution of the thesis, and the adaptation of the number of particles used within a particle MCMC algorithm provides a general purpose algorithm for sampling from a variety of complex distributions.

L’augmentation de la densité et la réduction de la tension d’alimentation des circuits intégrés rend la contribution des effets singuliers induits par les radiations majoritaire dans la diminution de la fiabilité des composants électroniques aussi bien dans l’environnement radiatif spatial que terrestre. Cette étude porte sur la modélisation des mécanismes physiques qui conduisent à ces aléas logiques (en anglais "Soft Errors"). Ces modèles sont utilisés dans une plateforme de simulation,appelée TIARA (Tool suIte for rAdiation Reliability Assessment), qui a été développée dans le cadre de cette thèse. Cet outil est capable de prédire la sensibilité de nombreuses architectures de circuits (SRAM,Flip-Flop, etc.) dans différents environnements radiatifs et sous différentes conditions de test (alimentation, altitude, etc.) Cette plateforme a été amplement validée grâce à la comparaison avec des mesures expérimentales effectuées sur différents circuits de test fabriqués par STMicroelectronics. La plateforme TIARA a ensuite été utilisée pour la conception de circuits durcis aux radiations et a permis de participer à la compréhension des mécanismes des aléas logiques jusqu’au noeud technologique 20nm. / Aggressive integrated circuit density increase and power supply scaling have propelled Single Event Effects to the forefront of reliability concerns in ground-based and space-bound electronic systems. This study focuses on modeling of Single Event physical phenomena. To enable performing reliability assessment, a complete simulation platform named Tool suIte for rAdiation Reliability Assessment (TIARA) has been developed that allows performing sensitivity prediction of different digital circuits (SRAM, Flip-Flops, etc.) in different radiation environments and at different operating conditions (power supply voltage,altitude, etc.) TIARA has been extensively validated with experimental data for space and terrestrial radiation environments using different test vehicles manufactured by STMicroelectronics. Finally, the platform has been used during rad-hard digital circuits design and to provide insights into radiation-induced upset mechanisms down to CMOS 20nm technological node.