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Early mammalian development before the oocyte-to-embryo transition is under 'maternal control' from factors deposited in the cytoplasm during oocyte growth, synthesized independent of de novo transcription. Maternal mRNAs encode proteins necessary for early embryo development. Two elements in the mRNA 3’untranslated region (UTR), the cytoplasmic polyadenylation element (CPE) and the hexanucleotide (AAUAAA) are involved in the control of translation of specific mRNAs during meiotic maturation. Despite advances in understanding the translational regulation during meiotic maturation, regulation at the 1-cell stage has not been explained. More studies are required to explain this complex mechanism of temporal mRNA recruitment after fertilization. Maternal mRNAs translated at different stages were examined to understand how specific maternal mRNAs are synthesized and stored, what are these maternal mRNAs, which maternal mRNAs are translated, and how these maternal mRNAs are temporally regulated. Polysomal mRNAs from eggs and 1-cell embryos were analyzed by microarray analysis and this indicated that temporally significant biological activities were encoded by mRNAs recruited at different stages of development. The mRNAs recruited in eggs were involved in homeostasis and transport mechanisms and those recruited in zygotes were involved in biosynthesis and metabolic activities. These data indicated that there is a temporal regulation of maternal mRNAs to meet the different biological requirements of the embryos. After the identification of temporally translated mRNAs, experiments were performed to understand the mechanism underlying temporal translation. The prevalence of the CPE differed between the two mRNA populations translated i.e., egg and 1-cell stage polysomal mRNAs. CPEs were present in ~53% of transcripts at the 1-cell stage compared to ~86% at the MII stage. This indicated that novel motifs other than CPEs regulate translation of mRNAs at the 1-cell stage. Truncation and deletion experiments were conducted using chimeric mRNAs based on one mRNA that was enriched in the 1- cell polysomes (Bag4). These experiments led to the identification of two regulatory regions that control translation at the 1-cell stage, an 80 nt region and a 43 nt region with different regulatory motifs. The 80 nt region is involved in activation of translation and the 43 nt region has an inhibitory effect on translation at the MII and early 1-cell stage. These results provide a detailed picture of how specific maternal mRNAs are prevented from undergoing translation at the MII stage and how the effect of inhibition is eliminated by the late 1-cell stage. / Biochemistry

The goal of this research is to construct causal gene networks for genetical genomics experiments using expression Quantitative Trait Loci (eQTL) mapping and Structural Equation Modeling (SEM). Unlike Bayesian Networks, this approach is able to construct cyclic networks, while cyclic relationships are expected to be common in gene networks. Reconstruction of gene networks provides important knowledge about the molecular basis of complex human diseases and generally about living systems. In genetical genomics, a segregating population is expression profiled and DNA marker genotyped. An Encompassing Directed Network (EDN) of causal regulatory relationships among genes can be constructed with eQTL mapping and selection of candidate causal regulators. Several eQTL mapping approaches and local structural models were evaluated in their ability to construct an EDN. The edges in an EDN correspond to either direct or indirect causal relationships, and the EDN is likely to contain cycles or feedback loops. We implemented SEM with genetics algorithms to produce sub-models of the EDN containing fewer edges and being well supported by the data. The EDN construction and sparsification methods were tested on a yeast genetical genomics data set, as well as the simulated data. For the simulated networks, the SEM approach has an average detection power of around ninety percent, and an average false discovery rate of around ten percent. / Ph. D.

Functional genetics is the study of the genes present in a genome of an organism, the complex interplay of all genes and their environment being the primary focus of study. The motivation for such studies is the premise that gene expression patterns in a cell are characteristic of its current state. The availability of the entire genome for many organisms now allows scientists unparalleled opportunities to characterize, classify, and manipulate genes or gene networks involved in metabolism, cellular differentiation, development, and disease. System-wide studies of biological systems have been made possible by the advent of high-throughput and large-scale tools such as microarrays which are capable of measuring the mRNA levels of all genes in a genome. Tools and methods for the integration, visualization, and modeling of the large-scale data obtained in typical systems biology experiments are indispensable. Our work focuses on a method that integrates gene expression values obtained from microarray experiments with biological functional information related to the genes measured in order to make global comparisons of multiple experiments. In our method, the integrated data is represented as a lattice and, using appropriate measures, a reference experiment can be compared to samples from a database of similar experiments, and a ranking of similarity is returned. In this work, support for the validity of our method is demonstrated both theoretically and empirically: a mathematical description of the lattice structure with respect to the integrated information is developed and the method is applied to data sets of both simulated and reported microarray experiments. A fast algorithm for constructing the lattice representation is also developed. / Ph. D.

Pathway diagrams, similar to the graph diagrams using a node-link representation, are used by biologists to represent complex interactions at the molecular level in living cells. The recent shift towards data-intensive bioinformatics and systems-level science has created a strong need for advanced pathway visualization tools that support exploratory data analysis. User studies suggest that an important requirement for biologists is the need to associate microarray data to pathway diagrams. A design space for visualization tools that allow analysis of microarray data in pathway context was identified for a systematic evaluation of the visualization alternatives. The design space is divided into two dimensions. Dimension 1 is based on the method used to overlay data attributes onto pathway nodes. The three possible approaches are: overlay of data on pathway nodes one data attribute at a time by manipulating a visual property (e.g. color) of the node, along with sliders or some such mechanism to animate the pathway for other timepoints. In another approach data from all the attributes in data can be overlaid simultaneously by embedding small charts (e.g., line charts or heatmap) into pathway nodes. The third approach uses miniature version of the pathways-as-glyph view for each attribute in the data. Dimension 2 decides if additional view besides pathway diagrams were used. These pathway visualizations are often linked to other type of visualization methods (e.g., parallel co-ordinates) using the concept of brushing and linking. The visualization alternatives from pathway + microarray data design space were evaluated by conducting two independent user studies. Both the studies used timeseries datasets. The first study used visualization alternatives from both dimension 1 and dimension 2. The results suggest that the method to overlay multidimensional data on pathway nodes has a non trivial influence on accuracy of participants' responses, whereas the number of visualizations affect participants' performance time for pre-selected tasks. The second study used visualization alternatives from dimension 1 that focuses on method used to overlay data attributes on pathway nodes. The study suggests that participants using pathway visualization that display data one attribute at a time on nodes have more controlled performance for all type of tasks as compared to the participants using other alternatives. Participants using pathway visualization that display data in node-as-glyphs view have better performance for tasks that require analysis for a single node, and identifying outlier nodes. Whereas, pathway visualizations with pathways-as-glyph view provide better performance on tasks that require analysis of overall changes in the pathway, and identifying interesting timepoints in the data. An insight-based method was designed to evaluate visualization tools for real world biologists' data analysis scenarios. The insight-based method uses different quantifiable characteristics of an "insight" that can be measured uniformly across participants. These characteristics were identified based on observations of the participants analyzing microarray data in a pilot study. The insight-based method provides an alternative to traditional task-based methods. This is especially helpful for evaluating visualization tools on large and complicated datasets where designing tasks can be difficult. Though, the insight-based method was developed to empirically evaluate visualization tools for short term studies, the method can also be used in real world longitudinal studies that analyzes the usage of visualization tools by the intended end-users. / Ph. D.

Oxidative stress is a harmful condition in a cell, tissue, or organ, caused by an imbalance between reactive oxygen species or other oxidants and the capacity of antioxidant defense systems to remove them. These oxidants cause wide-ranging damage to macromolecules, including proteins, lipids, DNA and carbohydrates. Oxidative stress is an important pathophysiologic component of a number of diseases, such as Alzheimer's disease, diabetes and certain cancers. Cells contain effective defense mechanisms to respond to oxidative stress. Despite much accumulated knowledge about these responses, their kinetics, especially the kinetics of early responses is still not clearly understood. The Yap1 transcription factor is crucial for the normal response to a variety of stress conditions including oxidative stress. Previous studies on Yap1 regulation started to measure gene expression profile at least 20 minutes after the induction of oxidative stress. Genes and pathways regulated by Yap1 in early oxidative stress response (within 20 minutes) were not identified in these studies. Here we study the kinetics of early oxidative stress response induced by the cumene hydroperoxide (CHP) in Saccharomyces cerevisiae wild type and yap1 mutant. Gene expression profiles after exposure to CHP were obtained in controlled conditions using Affymetrix Yeast Genome S98 arrays. The oxidative stress response was measured at 8 time points along 120 minutes after the addition of CHP, with the earliest time point at 3 minute after the exposure. Statistical analysis methods, including ANOVA, k-means clustering analysis, and pathway analysis were used to analyze the data. The results from this study provide a dynamic resolution of the oxidative stress responses in S. cerevisiae, and contribute to a richer understanding of the antioxidant defense systems. It also provides a global view of the roles that Yap1 plays under normal and oxidative stress conditions. / Ph. D.

DNA chip technology has drawn tremendous attention since it emerged in the mid 90 s as a method that expedites gene sequencing by over 100-fold. DNA chip, also called DNA microarray, is a combinatorial technology in which different single-stranded DNA (ssDNA) molecules of known sequences are immobilized at specific spots. The immobilized ssDNA strands are called probes. In application, the chip is exposed to a solution containing ssDNA of unknown sequence, called targets, which are labeled with fluorescent dyes. Due to specific molecular recognition among the base pairs in the DNA, the binding or hybridization occurs only when the probe and target sequences are complementary. The nucleotide sequence of the target is determined by imaging the fluorescence from the spots. The uncertainty of background in signal detection and statistical error in data analysis, primarily due to the error in the DNA amplification process and statistical distribution of the tags in the target DNA, have become the fundamental barriers in bringing the technology into application for clinical diagnostics. Furthermore, the dye and tagging process are expensive, making the cost of DNA chips inhibitive for clinical testing. These limitations and challenges make it difficult to implement DNA chip methods as a diagnostic tool in a pathology laboratory. The objective of this dissertation research is to provide an alternative approach that will address the above challenges.. In this research, a label-free assay is designed and studied. Polystyrene (PS), a commonly used polymeric material, serves as the fluorescence agent. Probe ssDNA is covalently immobilized on polystyrene thin film that is supported by a reflecting substrate. When this chip is exposed to excitation light, fluorescence light intensity from PS is detected as the signal. Since the optical constants and conformations of ssDNA and dsDNA (double stranded DNA) are different, the measured fluorescence from PS changes for the same intensity of excitation light.. The fluorescence contrast is used to quantify the amount of probe-target hybridization. A mathematical model that considers multiple reflections and scattering is developed to explain the mechanism of the fluorescence contrast which depends on the thickness of the PS film. Scattering is the dominant factor that contributes to the contrast. The potential of this assay to detect single nucleotide polymorphism is also tested. / Ph. D.

The demands placed on scientific software are different from those placed on general purpose software, and as a result, creating software for science and for scientists requires a specialized approach. Much of software engineering practices have developed in situations in which a tool is desired to perform some definable task, with measurable and verifiable outcomes. The users and the developers know what the tool "should" do. Scientific software often uses unproven or experimental techniques to address unsolved problems. The software is often run on "experimental" High Performance Computing hardware, adding another layer of complexity. It may not be possible to say what the software should do, or what the results should be, as these may be connected to very scientific questions for which the software is being developed. Software development in this realm requires a deep understanding of the relevent scientific domain area. The present work describes applications resulting from a scientific software development process that builds upon detailed understanding of the scientific domain area. YODA is an application primarily for selecting microarray probe sequences for measuring gene expression. At the time of its development, none of the existing programs for this task satisfied the best-known requirements for microarray probe selection. The question of what makes a good microarray probe was a research area at the time, and YODA was developed to incorporate the latest understanding of these requirements, drawn from the research literature, into a tool that can be used by a research biologist. An appendix examines the response and use in the years since YODA was released. PEPR is a software system for inferring highly resolved whole-genome phylogenies for hundreds of genomes. It encodes a process developed through years of research and collaboration to produce some of the highest quality phylogenies available for large sets of bacterial genomes, with no manual intervention required. This process is described in detail, and results are compared with high quality results from the literature to show that the process is at least as successful as more labor-intensive manual efforts. An appendix presents additional results, including high quality phylogenies for many bacterial Orders. / Ph. D.

This work describes the development and application of genomic DNA tiling arrays: microarrays designed to represent all of the DNA comprising a chromosome or other genomic locus, regardless of the genes that may be annotated in the region of interest. Because tiling arrays are intended for the unbiased interrogation of genomic sequence, they enable the discovery of novel functional elements beyond those described by existing gene annotation. This is of particular importance in mapping the gene structures of higher eukaryotes, where combinatorial exon usage produces rare splice variants or isoforms expressed in low abundance that may otherwise elude detection. Issues related to the design of both oligonucleotide- and amplicon-based tiling arrays are discussed; the latter technology presents distinct challenges related to the selection of suitable amplification targets from genomic DNA. Given the widespread fragmentation of mammalian genomes by repetitive elements, obtaining maximal coverage of the non-repetitive sequence with a set of fragments amenable to high-throughput polymerase chain reaction (PCR) amplification represents a non-trivial optimization problem. To address this issue, several algorithms are described for the efficient computation of optimal tile paths for the design of amplicon tiling arrays. Using these methods, it is possible to recover an optimal tile path that maximizes the coverage of non-repetitive DNA while minimizing the number of repetitive elements included in the resulting sequence fragments. Tiling arrays were constructed and used for the chromosome- and genome-wide assessment of human transcriptional activity, via hybridization to complementary DNA derived from polyadenylated RNA expressed in normal complex tissues. The approach is first demonstrated with amplicon arrays representing all of the non-repetitive DNA of human chromosome 22, then extended to the entire genome using maskless photolithographic DNA synthesis technology. A large-scale tiling array survey revealed the presence of over 10,000 novel transcribed regions and verified the expression of nearly 13,000 predicted genes, providing the first global transcription map of the human genome. In addition to those likely to encode protein sequences on the basis of evolutionary sequence conservation, many of the novel transcripts constitute a previously uncharacterized population of non-coding RNAs implicated in myriad structural, catalytic and regulatory functions.

As aquaculture continues to contribute high quality protein to a greater proportion of the worlds growing population, fish producers have been pressured to increase overall production. However, associated with elevated production is greater stress due to crowding, reduced water quality, and other factors. These stressors impact the health and welfare of the farmed animal which has become of increasing concern to a more environmentally aware and health conscious consumer. New strategies must therefore be developed and adopted by the aquaculture industry to counteract negative consumer perceptions of industrial fish production while also stabilizing the industry. Better nutrition may enhance disease resistance of farmed fish, while fillet accumulations of specific health-related nutrients may simultaneously add value to the final product. This thesis summarizes research undertaken in an effort to enhance the nutritional value of fish by increasing fillet levels of selenium (Se). In addition, various biomarkers of fish health (lysozyme, ceruloplasmin and glutathione peroxidase (GSH-Px) activities), were examined to determine whether dietary Se supplementation had a positive impact upon fish immunocompetence. Moreover, the effect of vaccination was also examined using lysozyme and growth as indicators of fish performance. Hybrid striped bass (HSB), the fourth most valuable farmed fish and fifth in tonnage produced in the United States, were employed as a model animal. Se, an essential component of the antioxidant enzyme, glutathione peroxidase with many established health benefits was supplemented to HSB diets at various concentrations but was found to be without effect upon serum immune proteins or GSH-Px activity. This finding likely reflected the use of fishmeal within the dietary formulation, which possessed relatively high Se levels, together with sufficient storage of tissue Se within the experimental animals. Nevertheless, these studies determined that organic sources of Se were more efficiently accumulated in HSB muscle than traditional inorganic sources. A linear response occurred up to the highest dose used (3.2 mg kg⁻¹) over a 6 week study. Fillet Se accumulation (r²=0.95) proved to be a better indicator than the liver (r²=0.87).Se enhanced fish therefor appear to offer a route of entry for fish producers into the lucrative designer food market - especially since many hundreds of millions of people worldwide are believed to be Se-deficient. Studies undertaken with Se-deficient HSB confirmed findings from the aforementioned research and also indicate that Se-enhanced fillets might be produced using a finishing feed containing 1.5 mg Se kg⁻¹ 6-8 weeks prior to harvest. Accumulation of Se using this strategy resulted in a 100g portion of HSB fillets containing between 33-109 µg Se, amounting to a dietary intake of between 25-80 µg Se; a level that would satisfy present daily intake recommendations. Vaccination of HSB with a Streptococcus iniae oil-in-water vaccine was examined for its potential negative impacts upon HSB production performance. Vaccinated fish did not exhibit any significant reductions in growth but microarray studies revealed that together with many hundreds of genes, four immune-related genes were impacted by this procedure. This thesis discusses the results obtained with regard to their practical implications to the industry and welfare of cultured fish. / Master of Science