<p> Neural stem progenitor cells (NSPCs) in the mammalian brain contribute to life-long neurogenesis and brain health. Adult mammalian neurogenesis primarily occurs in the subventricular zone (SVZ) and the subgranular zone (SGZ) of the dentate gyrus. Epigenetic repression is a crucial regulator of cell fate specification during adult neurogenesis. How epigenetic repression impacts adult neurogenesis and how epigenetic dysregulation may impact neoplasia or tumorigenesis remains poorly understood. Examination of epigenetic regulation in the adult mammalian brain is complicated by the heterogeneous nature of neurogenic niches and by the highly orchestrated fate specification processes within neural stem progenitor cells involving myriad intrinsic and extrinsic factors. To overcome these challenges, we utilized a cross-species approach. To model histone modifications as they exist <i>in vivo</i> for epigenetic profiling, we isolated neural stem progenitor cells from the adult SVZ and SGZ of non-human primate baboon brains. To determine cellular and molecular changes within the adult SVZ and SGZ following loss of epigenetic repression, we utilized multiple mouse models, including conditional <i> Ezh2</i> and <i>Suv4-20h1</i> knockouts. To model the non-cell type specific effects common to small molecule screening and brain chemotherapeutic agents, induction of conditional knockout utilized a recombinant Cre protein. Finally, to model epigenetic mechanisms during SVZ-associated glioblastoma (GBM) tumorigenesis, we conducted comparative analysis between healthy NSPCs and GBM specimens from humans. The convergence of baboon, mouse and human models of adult neurogenesis revealed that epigenetic repression is a critical mechanism regulating proper neural cell fate and that epigenetic dysregulation may be a driver of GBM.</p><p>

Identification and characterization of regions influencing the precise spatial and temporal expression of genes is critical to our understanding of gene regulatory networks. Connecting transcription factors to the cis-regulatory elements that they bind and regulate remains a challenging problem in computational biology. The rapid accumulation of whole genome sequences and genome-wide expression data, and advances in alignment algorithms and motif-finding methods, provide opportunities to tackle the important task of dissecting how genes are regulated. Genes exhibiting similar expression profiles are often regulated by common transcription factors. We developed a method for identifying statistically over-represented regulatory motifs in the promoters of co-expressed genes using weight matrix models representing the specificity of known factors. Application of our methods to yeast fermenting in grape must revealed elements that play important roles in utilizing carbon sources. Extension of the method to metazoan genomes via incorporation of comparative sequence analysis facilitated identification of functionally relevant binding sites for sets of tissue-specific genes, and for genes showing similar expression in large-scale expression profiling studies. Further extensions address alternative promoters for human genes and coordinated binding of multiple transcription factors to cis-regulatory modules. Sequence conservation reveals segments of genes of potential interest, but the degree of sequence divergence among human genes and their orthologous sequences varies widely. Genes with a small number of well-distinguished, highly conserved non-coding elements proximal to the transcription start site may be well-suited for targeted laboratory promoter characterization studies. We developed a “regulatory resolution” score to prioritize lists of genes for laboratory gene regulation studies based on the conservation profile of their promoters. Additionally, genome-wide comparisons of vertebrate genomes have revealed surprisingly large numbers of highly conserved non-coding elements (HCNEs) that cluster nearby to genes associated with transcription and development. To further our understanding of the genomic organization of regulatory regions, we developed methods to identify HCNEs in insects. We find that HCNEs in insects have similar function and organization as their vertebrate counterparts. Our data suggests that microsynteny in insects has been retained to keep large arrays of HCNEs intact, forming genomic regulatory blocks that surround the key developmental genes they regulate. / Medicine, Faculty of / Medical Genetics, Department of / Graduate

Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based on linear models for individual probe signals and using dynamic variable selection at the prediction level. The algorithms combine separate analyses based on the multiple probe sets to give a final confidence score for each candidate genotypes. Our proposed classification model achieved an accuracy level of >99.4% with 100% call rate for the SNP genotype data which is comparable with existing genotyping technologies. We discussed the appropriateness of the proposed model related to other existing high-throughput genotype calling algorithms. In this thesis we have explored three new ideas for classification with high dimensional data: (1) ensembles of various sets of predictors with built-in dynamic property; (2) robust classification at the prediction level; and (3) a proper confidence measure for dealing with failed predictor(s). We found that a mixture model for classification provides robustness against outlying values of the explanatory variables. Furthermore, the algorithm chooses among different sets of explanatory variables in a dynamic way, prediction by prediction. We analyzed several data sets, including real and simulated samples to illustrate these features. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any ‘bad data’ corresponding to image artifacts on the microarray slide or failure of a specific chemistry. Though motivated by this genotyping application, the proposed methodology would apply to other classification problems where the explanatory variables fall naturally into groups or outliers in the explanatory variables require variable selection at the prediction stage for robustness. / Science, Faculty of / Statistics, Department of / Graduate

Sequence features play an important role in the regulation of translation initiation. This thesis focuses on the sequence features affecting eukaryotic initiation. The characteristics of 5' untranslated region in Saccharomyces cerevisiae were explored. It is found that the 40 nucleotides upstream of the start codon is the critical region for translation initiation in yeast. Moreover, this thesis attempted to solve some controversies related to the start codon context. Two key nucleotides in the start codon context are the third nucleotide upstream of the start codon (-3 site) and the nucleotide immediately following the start codon (+4 site). Two hypotheses regarding +4G (G at +4 site) in Kozak consensus, the translation initiation hypothesis and the amino acid constraint hypothesis, were tested. The relationship between the -3 and +4 sites in seven eukaryotic species does not support the translation initiation hypothesis. The amino acid usage at the position after the initiator (P1' position) compared to other positions in the coding sequences of seven eukaryotic species was examined. The result is consistent with the amino acid constraint hypothesis. In addition, this thesis explored the relationship between +4 nucleotide and translation efficiency in yeast. The result shows that +4 nucleotide is not important for translation efficiency, which does not support the translation initiation hypothesis. This work improves our current understanding of eukaryotic translation initiation process.

Over the past few years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Genetic networks are one example where molecular-level fluctuations are of particular importance. Here stochasticity in the expression of gene products can result in genetically identical cells in the same environment displaying significant variation in biochemical or physical attributes. This variation can influence individual and population-level fitness. In this thesis we first explore the background required to obtain analytical solutions and perform simulations of stochastic models of gene expression. Then we develop an algorithm for the stochastic simulation of gene expression and heterogeneous cell population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo approach to simulate the statistical characteristics of growing cell populations. This approach permits biologically realistic and computationally feasible simulations of environment and time-dependent cell population dynamics. The algorithm is benchmarked against steady-state and time-dependent analytical solutions of gene expression models, including scenarios when cell growth, division, and DNA replication are incorporated into the modelling framework. Furthermore, using the algorithm we obtain the steady-state cell size distribution of a large cell population, grown from a small initial cell population undergoing stochastic and asymmetric division, to the size distribution of a small representative sample of this population simulated to steady-state. These comparisons demonstrate that the algorithm provides an accurate and efficient approach to modelling the effects of complex biological features on gene expression dynamics. The algorithm is also employed to simulate expression dynamics within 'bet-hedging' cell populations during their adaption to environmental stress. These simulations indicate that the cell population dynamics algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details, and phenotypic variability and fitness.

In a hierarchical categorization problem, categories are partially ordered to form a hierarchy. In this dissertation, we explore two main aspects of hierarchical categorization: learning algorithms and performance evaluation. We introduce the notion of consistent hierarchical classification that makes classification results more comprehensible and easily interpretable for end-users. Among the previously introduced hierarchical learning algorithms, only a local top-down approach produces consistent classification. The present work extends this algorithm to the general case of DAG class hierarchies and possible internal class assignments. In addition, a new global hierarchical approach aimed at performing consistent classification is proposed. This is a general framework of converting a conventional "flat" learning algorithm into a hierarchical one. An extensive set of experiments on real and synthetic data indicate that the proposed approach significantly outperforms the corresponding "flat" as well as the local top-down method. For evaluation purposes, we use a novel hierarchical evaluation measure that is superior to the existing hierarchical and non-hierarchical evaluation techniques according to a number of formal criteria. Also, this dissertation presents the first endeavor of applying the hierarchical text categorization techniques to the tasks of bioinformatics. Three bioinformatics problems are addressed. The objective of the first task, indexing biomedical articles with Medical Subject Headings (MeSH), is to associate documents with biomedical concepts from the specialized vocabulary of MeSH. In the second application, we tackle a challenging problem of gene functional annotation from biomedical literature. Our experiments demonstrate a considerable advantage of hierarchical text categorization techniques over the "flat" method on these two tasks. In the third application, our goal is to enrich the analysis of plain experimental data with biological knowledge. In particular, we incorporate the functional information on genes directly into the clustering process of microarray data with the outcome of an improved biological relevance and value of clustering results.

Microarray technology has been used as a routine high-throughput tool in biological research to characterize gene expression, and overwhelming volumes of data are generated in every microarray experiment as a consequence. However, there are many kinds of non-biological variations and systematic biases in microarray data which can confound the extraction of the true signals of gene expression. Thus comprehensive bioinformatic and statistical analyses are crucially required, typically including normalization, regulated gene identification, clustering and meta-analysis. The main purpose of my study is to develop robust analytical methods and programs for spotted cDNA-type microarray data. First, I established a novel normalization method based on the Generalized Procrustes Analysis (GPA) algorithm. I compared the GPA-based method with six other popular normalization methods, including Global, Lowess, Scale, Quantile, Variance Stabilization Normalization, and one boutique array-specific housekeeping gene method by using several different empirical criteria, and demonstrated that the GPA-based method was consistently better in reducing across-slide variability and removing systematic bias. In particular, being free from the biological assumptions that most genes (95%) are not differentially expressed on the array, the GPA method is therefore more robust, and appropriate for diverse types of array sets, including the boutique array where the majority of genes may be differentially expressed. Second, I utilized statistical analysis to assess the quality of a novel goldfish brain cDNA microarray, which provides statistical validation of microarray data result. Thirdly, I developed a new program suite as a user-friendly analytical pipeline integrating most popular analytical methods for microarray data analysis. Finally, I proposed a novel analytical strategy to extract season-related gene expression information from multiple microarray data sets by using comprehensive data transformation and normalization analysis, differential gene identification, and multivariate analysis.

Due the sheer size and complexity of genomes, it is essential to develop automated methods to analyze them. To compare genomes, one distance measure that has been proposed is to determine the minimum number of evolutionary changes needed to transform one genome into another. In recent years, great progress has been made in this area with efficient exact algorithms that can transform one genome to another applying a wide range of evolutionary operations. However, gene duplications, a common occurrence and arguably the most important evolutionary operation, have proven to be one of the most difficult evolutionary operations to integrate. We examine the most successful gene duplication algorithms: a family of algorithms that we call the rearrangement-duplication algorithms. Rather than compare two genomes, these algorithms attempt to efficiently remove the duplicates from a genome using the fewest number of duplications and other evolutionary operations. In this thesis we give a complete survey of all the genome halving algorithms, a highly successful group of rearrangement-duplication algorithms that efficiently and exactly handle whole genome doubling ( tetraploidization). We also introduce the genome aliquoting algorithms, a new variation on the genome halving problem, that attempts to handle unlimited scale whole genome duplications. As a new and challenging problem there are currently no efficient exact algorithms. However, early results include two approximation algorithms.

As genomes evolve over hundreds of millions years, the chromosomes become rearranged, with segments of some chromosomes inverted, while other chromosomes reciprocally exchange chunks from their ends. These rearrangements lead to the scrambling of the elements of one genome with respect to another descended from a common ancestor. Multidisciplinary work undertakes to mathematically model these processes and to develop statistical analyses and mathematical algorithms to understand the scrambling in the chromosomes of two or more related genomes. A major focus is the reconstruction of the gene order of the ancestral genomes. There has been a trend in increasing the phylogenetic scope of genome sequencing without finishing the sequence for each genome. With less interest in completing the sequence, there is an increasing number of genomes being published in scaffold or even contig form. Rearrangement algorithms, including gene order-based phylogenetic tools, require whole genome data on gene order or syntenic block order. Then, for gene order-based comparisons or phylogeny, how can we use rearrangement algorithms to handle genomes available in contig or scaffold form only? For contig data, we develop a model for the behaviour of the genomic distance as a function of evolutionary time, and discuss how to invert this function in order to infer elapsed time. We show how to correct for the effect of chromosomal fragmentation in sets of contigs. We apply our methods to data originating mostly in the 12-genome Drosophila project [15]. We compare ten Drosophila genomes with two other dipteran genomes and two outgroup insect genomes. For scaffolds, our method involves optimally filling in genes missing in the scaffolds, and using the augmented scaffolds directly in the rearrangement algorithms as if they were chromosomes, while making a number of corrections, e.g., we correct for the number of extra fusion/fission operations required to make scaffolds comparable to full assemblies. We model the relationship between scaffold density and genomic distance, and estimate the parameters of this model while comparing the angiosperms genomes Ricinus communis and Vitis vinifera. A separate question arises of what the biological consequences of breakpoint creation are, rather than just their structural aspects. The question I will ask is whether proximity to the site of a breakpoint event changes the activity of a gene. I propose to investigate this by comparing the distribution of distances to the nearest breakpoint of genes that change expression in human versus the distribution of genes that do not change expression in human, compared to other primate species (e.g. macaque or chimpanzee). Keywords: chromosome rearrangement, comparative genomics, phylogenomics, phylogenetic tree, inversion, reciprocal translocation, transposition, DCJ, breakpoint, gene expression.

Frequent subgraph mining is a useful method for extracting biologically relevant patterns from a set of graphs or a single large graph. Here, the graph represents all possible RNA structures and interactions. Patterns that are significantly more frequent in this graph over a random graph are extracted. We hypothesize that these patterns are most likely to represent a biological mechanisms. The graph representation used is a directed dual graph, extended to handle intermolecular interactions. The graph is sampled for subgraphs, which are labeled using a canonical labeling method and counted. The resulting patterns are compared to those created from a randomized dataset and scored. The algorithm was applied to the mitochondrial genome of the kinetoplastid species Trypanosoma brucei. This species has a unique RNA editing mechanism that has been well studied, making it a good model organism to test RiboFSM. The most significant patterns contain two stem-loops, indicative of gRNA, and represent interactions of these structures with target mRNA.