Dialkylstannylene acetals are tin-containing species employed extensively as intermediates to facilitate high-yielding and regioselective monosubstitution reactions of diols or polyols with various electrophiles, which is an important application of organotin compounds in organic synthesis. Although an abundance of experimental studies of these reactions have been reported, the mechanism of the reaction has not been well defined. High-level theoretical methods are used in this thesis to investigate the chemistry of organotin systems at a molecular level. This involves the exploration of the geometry characteristics of the gas-phase structures along the reaction paths in order to understand the mechanism of the organotin-mediated alkylations of diols. Alkylation reactions which require strict conditions can be dramatically enhanced by the presence of nucleophiles. The effects of added nucleophiles were examined computationally by comparing reaction profiles obtained for alkylations of dimethylstannylene acetals in the presence of different nucleophiles.

The hippocampus is a brain region that is critically involved in memory formation. Stratum oriens-lacunosum/moleculare (O-LM) interneurons have been shown to modulate incoming sensory information onto principal cells in CA1. Multi-compartment computational models of O-LM cells have been developed to better understand their functional roles in network contexts. Due to the variability and incompleteness of experimental details, however, a population of models that collectively captures intrinsic O-LM cell behavior is needed. We generated a database of O-LM models with physiologically plausible ranges for conductance densities using NEURON simulations on a supercomputer cluster. A subset of models that best represented O-LM cell electrophysiological output was subsequently extracted from the database and analyzed in order to determine correlations in conductance densities. Three major co-regulatory balances were found, which provide specific hypotheses for experimental investigations and point to the possibility of identifying a “signature” of conductance density balances for particular neuronal cell types.

Identifying single nucleotide polymorphisms (SNPs) that are involved in common and complex diseases, such as cancer, is a major challenge in current molecular epidemiology. Knowledge of such SNPs is expected to enable timely diagnosis, effective treatment, and, ultimately, prevention of human disease. However, the tremendous number of SNPs on the human genome, which is estimated at more than eleven million, poses challenges to obtain and analyze the information of all the SNPs. In this thesis we address the problem of selecting representative SNP markers for supporting effective disease-gene association studies. Our goal is to facilitate the genotyping and analysis procedure, associated with such studies, by providing effective prioritization methods for SNP markers based on both their allele information and functional significance. However, the problem of SNP selection has been proven to be NP-hard in general, and current selection methods impose certain restrictions and use heuristics for reducing the complexity of the problem. We thus aim to develop new heuristic algorithms and systems to advance the state-of-the-art, while relaxing the restrictions. To address this challenge, we formulate several SNP selection problems and present novel algorithms and a database system based on the two major SNP selection approaches: tag SNP selection and functional SNP selection. Furthermore, we describe an innovative approach to combine both tag SNP selection and functional SNP selection into one unified selection process. We demonstrate the improved performance of all the proposed methods using comparative studies. / Thesis (Ph.D, Computing) -- Queen's University, 2009-06-22 15:26:14.061

The Engage to Excel (PCAST) report, the National Research Council's Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation's ability for connecting scientific practice to the high school science classroom.

The hippocampus is a brain region that is critically involved in memory formation. Stratum oriens-lacunosum/moleculare (O-LM) interneurons have been shown to modulate incoming sensory information onto principal cells in CA1. Multi-compartment computational models of O-LM cells have been developed to better understand their functional roles in network contexts. Due to the variability and incompleteness of experimental details, however, a population of models that collectively captures intrinsic O-LM cell behavior is needed. We generated a database of O-LM models with physiologically plausible ranges for conductance densities using NEURON simulations on a supercomputer cluster. A subset of models that best represented O-LM cell electrophysiological output was subsequently extracted from the database and analyzed in order to determine correlations in conductance densities. Three major co-regulatory balances were found, which provide specific hypotheses for experimental investigations and point to the possibility of identifying a “signature” of conductance density balances for particular neuronal cell types.

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The cerebral tissue requires a constant supply of oxygen and nutrients. This is maintained through delivering a constant supply of blood. The delivery of sufficient blood is preserved by the cerebral vasculature and its autoregulatory function. The cerebral vasculature is composed of the Circle of Willis (CoW), a ring-like anastomoses of arteries at the base of the brain, and its peripheral arteries. However, only 50% of the population have a classical complete CoW network. This implies that the route of blood flow through the cerebral vasculature is different and dependent on where the blood is needed most in the brain. Autoregulation is a mechanism held by the peripheral arteries and arterioles downstream of the CoW. It ensures the delivery of the essential amount of cerebral blood flow despite changes in the arterial perfusion pressure, through the vasoconstriction and vasodilation of the vessels. The mechanisms that control the vessels’ vasomotion could be attributed to myogenic, metabolic, neurogenic regulation or a combination of all three. However, the variations in the CoW structure, combined with different pathological conditions such as hypertension, a stenosis or an occlusion in one or more of the supplying cerebral arteries may alter, damage or abolish autoregulation, and consequently result in a stroke. Stroke is the most common cerebrovascular disease that affects millions of people in the world every year. Therefore, it is essential to understand the cerebral hemodynamics via mathematical modelling of the cerebral vasculature and its regulation mechanisms. This thesis presents the developed model of the cerebral vasculature coupled with the different forms of autoregulation mechanisms. The model was developed over multiple stages. First, a linear model of the CoW was developed, where the peripheral vessels downstream of the CoW efferent arteries are represented as lumped parameter variable resistances. The autoregulation function in the efferent arteries was modelled using a PI controller, and a metabolic model was added to the lumped peripheral variable resistances. The model was then modified so the pressure losses encountered at the CoW bifurcations, and the vessels’ tortuosity are taken into account resulting in a non-linear system. A number of cerebral autoregulation models exist in the literature, however, no model combines a fully populated arterial tree with dynamic autoregulation. The final model presented in this thesis was built by creating an asymmetric binary arterial vascular tree to replace the lumped resistance parameters for the vasculature network downstream of each of the CoW efferent arteries. The autoregulation function was introduced to the binary arterial tree by implementing the myogenic and metabolic mechanisms which are active in the small arteries and arterioles of the binary arterial tree. The myogenic and metabolic regulation mechanisms were both tested in the model. The results indicate that because of the low pressures experienced by the arterioles downstream of the arterial tree, the myogenic mechanism, which is hypothesised by multiple researchers as the main driver of autoregulation, does not provide enough regulation of the arterioles’ diameters to support autoregulation. The metabolic model showed that it can provide sufficient changes in the arterioles’ diameters, which produces a vascular resistance that support the constancy of the autoregulation function. The work carried out for this research has the potential of being a significant clinical tool to evaluate patient-specific cases when combined with the graphical user interfaces provided. The research and modelling performed was done as part of the Brain Group of the Centre of Bioengineering at the University of Canterbury.

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