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Carbohydrate and protein interactions are often essential in viral and bacterial infection, the immune response, cell differentiation and development, and the progression of tumor cell metastasis. Therefore, an understanding of carbohydrate–protein interactions at the molecular level would lead to a better insight into the biological process of living systems and assist in the development of therapeutic and diagnostic strategies. Our goal was to synthesize different mannose derivatives, immobilize them on nano-patterned surfaces and carry out binding studies with mannose-binding lectins in order to characterize carbohydrate–protein interactions. Different derivatives of D-mannose (monosaccharide, (1→2)-linked disaccharide, (1→3)-linked disaccharide, and (1→2, 1→3)-linked trisaccharide) with tethered –SH groups were synthesized. Alkyne-terminated D-mannose derivatives were synthesized to be immobilized via click chemistry on azide-functionalized glass slides. These molecules were constructed by glycosylation of appropriately protected glycosyl donors and acceptors, followed by free-radical addition to introduce the thiol terminals onto the aglycons. Subsequent deprotection afforded the corresponding free-OH saccharides. Standard robotic microarray printing technology was used to couple these thiol-terminated aglycons to epoxide-functionalized glass slides. Using a fluorescence scanner, binding between carbohydrates and Con A were quantified and processed to obtain dissociation constants (KD). The (1→2)-linked disaccharide 18 showed highest binding with Con A with dissociation constant of 58 nM [nano molar]. The (1→3)-linked disaccharide 24 had a dissociation constant of 68 nM [nano molar] with Con A. The differences in binding constants seem to be greater at higher concentrations above 400 μM [micro molar]. The monosaccharide 5 had an average surface dissociation constant of 76 nM [nano molar] and 91 nM [nano molar] for the trisaccharide 29. In general, the disaccharides 18 and 24 showed enhanced binding interaction with Con A than the monosaccharide 5 and trisaccharide 29.

<p>Double-stranded DNA breaks are potentially lethal lesions that can be repaired in mitotic cells by either homologous recombination (HR) or non-homologous end- joining (NHEJ) pathways. In the HR pathway, the broken DNA molecule is repaired using either the sister chromatid or the homolog as a template. Mitotic recombination events involving the homolog often result in loss of heterozygosity (LOH) of markers located distal to the crossover. In humans that are heterozygous for a mutation in a tumor suppressor gene, mitotic recombination leading to LOH can be an early step in cancer development.</p><p> In my thesis research, I analyzed mitotic recombination in the yeast Saccharomyces cerevisiae using oligonucleotide-containing microarrays to detect LOH of single-nucleotide polymorphisms (SNPs). In analyzing cells treated with ionizing radiation, I performed the first whole-genome analysis of LOH events done in any organism (Chapter 2). I showed that irradiated cells had between two and three unselected LOH events. I also showed that crossovers were often associated with non- reciprocal exchanges of genetic information (gene conversion events) and that these conversion events were more complex than predicted by standard models of homologous recombination.</p><p> In Chapter 3, I describe my mapping of spontaneous crossovers in a 1.1 Mb region of yeast chromosome IV. This analysis is the first high-resolution mitotic recombination map of a substantial fraction (about 10%) of a eukaryotic genome. I demonstrated the existence of recombination "hotspots" and showed that some of these hotspots were homolog-specific. Two of the strongest hotspots were formed by closely- spaced inverted repeats of retrotransposons. I demonstrated that the hotspot activity was a consequence of a secondary DNA structure formed by these repeats. Additionally, the majority of spontaneous LOH events reflect DNA lesions induced in unreplicated chromosomes during G1 of the cell cycle, indicating that G1-initiated lesions threaten genome stability more than G2-initiated lesions.</p><p> In Chapter 4, I describe mitotic crossovers associated with DNA replication stress induced by hydroxyurea (HU) treatment. Surprisingly, most HU-induced crossovers had conversion tracts indicative of DNA lesions initiated in G1. Additionally, HU- induced recombination events were very significantly associated with solo delta elements, a 330 bp sequence that is repeated several hundred times in the yeast genome.</p> / Dissertation

My PhD research focuses on the recommendation of optimal designs for two-colour microarray experiments. Two-colour microarrays are a technology used to investigate the behaviour of many thousands of genes in a single experiment. This technology has created the potential for making significant advances in the field of bioinformatics. Careful statistical design is crucial to realize the full potential of microarray technology. My research has focused on the recommendation of designs that are optimal in terms of precision for effects that are of scientific interest, making the most effective use of available resources. Based on statistical efficiency, the optimality criterion used is Pareto optimality. A design is defined to be Pareto optimal if there is no other design that leads to equal or greater precision for each effect of scientific interest and strictly greater precision for at least one. My PhD thesis was submitted in June and key aspects of my research are summarised below. Pareto optimality enables the recommendation of designs that are particularly efficient for the effects that are of scientific interest. I have developed methodology to cater for effects of interest that correspond to contrasts rather than solely considering parameters of the statistical linear model. My approach also caters for additional experimental considerations such as contrasts that are of equal scientific interest. During my PhD, I have provided advice regarding the design of two-colour microarray experiments aimed at discovering the genetic basis of medical conditions. For large experiments, it is not feasible to examine all possible designs in an exhaustive search for Pareto optimal designs. I have adapted the multiple objective metaheuristic method of Pareto simulated annealing to the microarray context. The aim of Pareto simulated annealing is to generate an approximation to the set of Pareto optimal designs in a relatively short time. At each iteration, a sample of generating designs is used to explore the design space in an efficient way. This involves the setting of a number of Pareto simulated annealing parameters and the development of appropriate quality measures. I have developed algorithms to search systematically for the optimal values of the tuning parameters based on Pareto simulated annealing and response surface methodology. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2010

Two-colour microarrays are a popular tool for measuring relative gene expression between RNA populations for thousands of genes simultaneously. This thesis develops methods for assessing the quality and variability of data from such experiments and for incorporating these assessments into algorithms for discovering differential expression. The variability of microarray data depends not only on the quality of the arrays, but also on how they are processed and normalised. The intimate relationship between variability of expression log-ratios and the method used for background correcting the expression values is specifically explored. The performance of different estimators of the background level and various model-based processing methods, including a novel normal-exponential convolution model are compared in search of a ‘best’ alternative. The results indicate that the choice of method should be guided by the specific question of interest; the model-based methods give gene expression measures with low bias, and do very well at choosing differentially expressed genes, while subtracting low background estimates, or not background correcting the data produces low variance estimates which are the most biased, however perform best at choosing DE genes. All of these alternatives give better results than those obtained by the standard approach of subtracting high local background estimates from the foreground signal, which is not recommended. (For complete abstract open document)

Thesis (Ph.D.)--Tufts University, 2001. / Adviser: David R. Walt. Submitted to the Dept. of Chemistry, Includes bibliographical references (leaves 197-208). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (M.S.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: Christa Merzdorf. Includes bibliographical references (leaves 51-55).

Thesis (Ph.D.)--University of Melbourne, Peter MacCallum Cancer Centre and The Dept. of Biochemistry and Molecular Biology, 2006. / Typescript. Includes bibliographical references (leaves 281-331).

Thesis (PhD) - Swinburne University of Technology, Industrial Research Institute Swinburne - 2006. / A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Industrial Research Institute Swinburne, Swinburne University of Technology - 2006. Typescript. Includes bibliographical references (p. 184-197).