Circadian rhythms are visible as daily oscillations in biochemical, physiological, or behavioral processes. These rhythms are produced by an endogenous clock that maintains synchrony with the external environment through responses to external stimuli such as light or temperature. The clock, in turn, coordinates internal processes in a time-dependent fashion. Genetic and molecular analysis of the filamentous fungus Neurospora crassa has demonstrated that the products of the frequency (frq) and white-collar (wc-1 and wc-2) genes interact to form an interlocked feedback loop that lies at the heart of the clock in this fungus. This feedback loop, termed the FRQ/WC oscillator, produces a ~24h oscillation in frq mRNA, FRQ protein, and WC-1 protein. In turn, the FRQ/WC oscillator regulates rhythmic behavior and gene expression. The goal of this dissertation is to understand how rhythmic outputs are regulated by the FRQ/WC oscillator in Neurospora. To this end, we have taken a microarray approach to first determine the extent of clock-controlled gene expression in Neurospora. Here, we show that circadian regulation of gene expression is widespread; 145 genes, representing 20% of the genes we analyzed, are clock-controlled. We show that clockregulation is complex; clock-controlled genes peak at all phases of the circadian cycle. Furthermore, we demonstrate the clock regulates diverse biological processes, such as intermediary metabolism, translation, sexual development and asexual development. WC-1 is required for all light- and clock-regulated gene expression in Neurospora. We have shown that overexpression of WC-1 is sufficient to activate clock-controlled gene expression, but is not sufficient to induce all light-regulated genes in Neurospora. This result indicates that cycling of WC-1 is sufficient to regulate rhythmic expression of a subset of clockcontrolled genes. Conversely, a post-translational mechanism underlies WC-1 mediated light signal transduction in Neurospora. Finally, we have demonstrated the Neurospora circadian system is comprised of mutually coupled oscillators that interact to regulate output gene expression in the fungus.

Bioinformatics applications can address the transfer of information at several stages of the central dogma of molecular biology, including transcription and translation. This dissertation focuses on using Bayesian models to interpret biological data in bioinformatics, using Markov chain Monte Carlo (MCMC) for the inference method. First, we use our approach to interpret data at the transcription level. We propose a two-level hierarchical Bayesian model for variable selection on cDNA Microarray data. cDNA Microarray quantifies mRNA levels of a gene simultaneously so has thousands of genes in one sample. By observing the expression patterns of genes under various treatment conditions, important clues about gene function can be obtained. We consider a multivariate Bayesian regression model and assign priors that favor sparseness in terms of number of variables (genes) used. We introduce the use of different priors to promote different degrees of sparseness using a unified two-level hierarchical Bayesian model. Second, we apply our method to a problem related to the translation level. We develop hidden Markov models to model linker/non-linker sequence regions in a protein sequence. We use a linker index to exploit differences in amino acid composition between regions from sequence information alone. A goal of protein structure prediction is to take an amino acid sequence (represented as a sequence of letters) and predict its tertiary structure. The identification of linker regions in a protein sequence is valuable in predicting the three-dimensional structure. Because of the complexities of both models encountered in practice, we employ the Markov chain Monte Carlo method (MCMC), particularly Gibbs sampling (Gelfand and Smith, 1990) for the inference of the parameter estimation.

Natural diseases caused by keratin mutations are rare and have only been reported in humans. We have recently identified a heritable skin disorder in Norfolk terriers caused by a mutation in KRT10. Affected dogs have a tendency to form shallow erosions or blisters following mild trauma, which is first noted after the birthing process. As the dogs age, they display generalized hyperpigmentation and scaling that is most severe in the axillary and inguinal regions. The main histologic and ultrastructural features include: marked hyperkeratosis, epidermal hyperplasia, prominent vacuolation of the upper suprabasal layers, eosinophilic intracytoplasmic aggregates (keratin bundles), numerous and frequently enlarged keratohyaline granules, and epidermal hyperplasia. Analysis of an extended pedigree through seven generations confirmed an autosomal recessive mode of inheritance. The keratin 10 mutation was defined as a G-T point mutation in intron 5 that affected splicing at the boundary of exon 4 and intron 5. The primary outcome of the mutation was a 35 bp deletion in exon 4 caused by use of a cryptic splice site. Real-time PCR quantitation of KRT10 confirmed that this mutation led to premature mRNA decay and an average 35-fold decrease in KRT10 message. Organotypic cell culture techniques were used to establish in vitro models for normal and affected Norfolk terriers. After 21 days of culture, normal epidermis was cornified with a compact and multifocally parakeratotic stratum corneum. Affected epidermis largely reproduced the expected morphologic alterations. Immunoblotting and immunohistochemistry for keratin 10 protein and real-time PCR quantitation of KRT10 message showed significantly less keratin expression in vitro than in vivo suggesting that the differentiation program in vitro underwent significant alterations. A diagnostic PCR assay was established for detection of the carrier state. Global analysis of gene expression between normal, carrier and affected dogs was performed with DermArray cDNA microarrays. Affected and carrier dogs showed differential regulation of 320 and 298 genes, respectively, between normal dogs. In affected dogs, 217 were upregulated and 103 were downregulated. In carrier dogs, 222 were upregulated and 76 were downregulated. 72 genes (65 upregulated, 7 downregulated) were altered in both affected and heterozygous dogs.

DNA Microarrays are one of the latest breakthroughs in experimental molecular biology and have opened the possibility of creating datasets of molecular information to represent many systems of biological or clinical interest. Clustering techniques have been proven to be helpful to understand gene function, gene regulation, cellular processes, and subtypes of cells. Investigations show that more often than not, several genes contribute to a disease, which motivates researchers to identify a subset of genes whose expression levels are similar under a subset of conditions. Most of the subspace clustering models define similarity among different objects by distances over either all or only a subset of the dimensions. However, strong correlations may still exist among a set of objects, even if they are far apart from each other as measured by the distance functions. Many techniques, such as pCluster and zCluster, have been proposed to find subspace clusters with the coherence expression of a subset of genes on a subset of conditions. However, both of them contain the time-consuming steps, which are constructing gene-pair MDSs and distributing the gene information in each node of a prefix tree. Therefore, in this thesis, we propose a Large Itemset-Based Clustering (LISC) algorithm to improve the disadvantages of the pCluster and zCluster algorithms. First, we avoid to construct the gene-pair MDSs. We only construct the condition-pair MDSs to reduce the processing time. Second, we transform the task of mining the possible maximal gene sets into the mining problem of the large itemsets from the condition-pair MDSs. We make use of the concept of the large itemset which is used in mining association rules, where a large itemset is represented as a set of items appearing in a sufficient number of transactions. Since we are only interested in the subspace cluster with gene sets as large as possible, it is desirable to pay attention to those gene sets which have reasonably large support from the condition-pair MDSs. In other words, we want to find the large itemsets from the condition-pair MDSs; therefore, we obtain the gene set with respect to enough condition-pairs. In this step, we efficiently use the revised version of FP-tree structure, which has been shown to be one of the most efficient data structures for mining large itemsets, to find the large itemsets of gene sets from the condition-pair MDSs. Thus, we can avoid the complex distributing operation and reduce the search space dramatically by using the FP-tree structure. Finally, we develop an algorithm to construct the final clusters from the gene set and the condition--pair after searching the FP-tree. Since we are interested in the clusters which are large enough and not belong to any other clusters, we alternately combine or extend the gene sets and the condition sets to construct the interesting subspace clusters as large as possible. From our simulation results, we show that our proposed algorithm needs shorter processing time than those previous proposed algorithms, since they need to construct gene-pair MDSs.

微陣列矩陣晶片是一門現今被廣泛使用在許多領域的生物醫學研究，在本文，我們主要是對寡核甘酸微陣列矩陣晶片資料的正規化感興趣。為了比較不同的正規化方法，我們致力於模擬更接近真實寡核甘酸微陣列矩陣晶片的資料。在資料的模擬上，我們主要是根據Li和Wong的模型來進行模擬，並利用階層法來設定模型的參數。最後為了判別正規化方法的好壞，我們模擬了100組資料，並且利用四個判斷準則來做比較。模擬的結果表示，我們所提出的新方法 (LOESS to Average)，一般來說都比其他的正規化方法來的好。 / Microarray technology is now widely used in many areas of biomedical research. In this thesis, we are interested in the normalization for oligonucleotide Microarray data. We aimed to simulate more realistic oligonucleotide microarry data in order to compare different normalization methods. The data simulation was based on Li and Wong's model with a hierarchical setup for parameters. In order to compare normalization methods, 100 data sets were simulated data. The performance of ten normalization methods was assessed based on four comparison criteria. Simulation results suggest that our new proposed normalization method, LOESS to Average, is generally a better method than other normalization methods.

Proteomics is a fast growing field and there has been a tremendous increase of knowledge the last two decades. Mass spectrometry is the most used method for analysis of complex protein samples. It can be used both in large scale discovery studies as well as in targeted quantitative studies. In parallel with the fast improvements of mass spectrometry-based proteomics there has been a fast growth of affinity-based methods. A common challenge is the large dynamic range of protein concentrations in biological samples. No method can today cover the whole dynamic range. If affinity and mass spectrometry-based proteomics could be used in better combination, this would be partly solved. The challenge for affinity-based proteomics is the poor specificity that has been seen for many of the commercially available antibodies. In mass spectrometry, the challenges are sensitivity and sample throughput. In this thesis, large scale approaches for validation of antibodies and other binders are presented. Protein microarrays were used in four validation studies and one was based on mass spectrometry. It is shown that protein microarrays can be valuable tools to check the specificity of antibodies produced in a large scale production. Mass spectrometry was shown to give similar results as Western blot and Immunohistochemistry regarding specificity, but did also provide useful information about which other proteins that were bound to the antibody. Mass spectrometry has many applications and in this thesis two methods contributing with new knowledge in animal proteomics are presented. A combination of high affinity depletion, SDS PAGE and mass spectrometry revealed 983 proteins in dog cerebrospinal fluid, of which 801 were marked as uncharacterized in UniProt. A targeted quantitative study of cat serum based on parallel reaction monitoring showed that mass spectrometry can be an applicable method instead of ELISA in animal proteomic studies. Mass spectrometry is a generic method and has the advantage of shorter and less expensive development costs for specific assays that are not hampered by cross-reactivity. Mass spectrometry supported by affinity based applications will be an attractive tool for further improvements in the proteomic field.

The genome sequencing programs have identified hundreds of thousands of genetic and proteomic targets for which there are presently no ascribed functions. The challenge for researchers now is to characterize them, as well as identify and characterize their natural variants. Historically, this has meant studying each individual target separately. However, due to the recent development of multi-analyte microarray devices, these characterizations can be performed in a combinatorial manner in which a single experiment provides information on thousands of targets at a time. In the past decade, microarray technology has settled in on two major designs. The first entails spotting individual receptor types onto a functionalized glass substrate. This is a simple and inexpensive process; however, due to the limited resolution of the mechanical devices used to do the spotting, the densities of these arrays are relatively low. Moreover, receptor preparation requires substantial time and effort. The second variety of microarray uses photolithographic techniques adapted from the semi-conductor industry to chemically synthesize the receptor elements in situ on the sensing surface. Because lithographic patterning is spatially very precise, these arrays achieve very high densities, with as many as one million features per square centimeter. Although these arrays obviate the necessity for laborious "off chip" probe preparation, they are expensive to produce and are limited to two types of receptors (oligonucleotides and peptides). This dissertation presents the development work performed on a hydrogel-based biosensor platform which provides a high density and low cost alternative to the two aforementioned designs. The array features are fabricated lithographically from a liquid pre-polymer doped with biologically active sensing elements at sizes as small as 50[micrometer]. Each of the feature types is uniquely shaped, which enables the features to be mass-produced in batches, pooled together and then assembled into randomly ordered arrays using highly-parallelized self-assembly techniques. The three-dimensional hydrogel features accommodate a wide variety of sensing elements, such as enzymes, antibodies and cells, which cannot be deployed using the traditional designs. This dissertation presents methods developed to integrate cellular and oligonucleotide sensing elements into the hydrogel features which preserve their biological activity and optimize the sensor's performance. / text

Ischemia is characterized by reduced blood flow to an area of the body which can then cause cellular injury through the generation of reactive oxygen species (ROS), activation of inflammation, and induction of apoptosis. Although rapid reestablishment of flow is required to prevent organ death, the reperfusion phase of this injury can cause its own deleterious effects often exacerbating the initial insult. The combined action of the two injuries is termed ischemia/reperfusion (I/R) injury. Oxidative stress that results from ischemia/reperfusion injury is a common pathological condition that accompanies many human diseases including stroke, heart attack and traumatic injury. In addition, neurodegenerative diseases including Parkinson’s, Alzheimer’s, and Huntington’s disease appear to involve oxidative stress. Although actively investigated by the medical and pharmaceutical industry; limited progress has been made to ameliorate I/R injury and to date there is no drug approved for treatment for I/R injury. Therapeutic approaches to treat I/R injury have included the administration of compounds to scavenge ROS or induce protective pathways or genetic responses. It was previously reported that caffeic acid phenethyl ester (CAPE), a plant-derived polyphenol, displayed cytoprotective effects against menadione (MD)-induced oxidative stress in human umbilical vein endothelial cells (HUVEC), and the induction of heme oxygenase-1 (HMOX1), a phase II enzyme, played an important role for CAPE cytoprotection. In an effort to improve this cytoprotection, other phase II enzyme inducers were investigated and, 2-cyano-3,12 dioxooleana-1,9 dien-28-imidazolide (CDDO-Im) and 2-cyano-3,12-dioxooleana-1,9-dien-28-oyl methyl ester (CDDO-Me), were found to be potent inducers with a rapid onset of action. CDDO-Im and CDDO-Me, synthetic olenane triterpenoids, developed as anticancer agents were compared to CAPE revealing that CDDO-Im was a more potent inducer of Phase II enzymes including HMOX1 and provided better cytoprotection than CAPE. Gene expression profiling showed that CDDO-Im was more potent inducer of protective genes like HMOX1 than CAPE and additionally induced heat shock proteins. To better understand the mechanism of action of CDDO-IM, a gene expression time-course was undertaken to identify early initiators of the transcriptional response preceding cytoprotection. Application of systems pharmacology identified molecular networks of cell mediating processes.

Skeletal muscle possesses remarkable plasticity in responses to altered mechanical load. An established murine model used to increase mechanical load on a muscle is the surgical removal of the gastrocnemius and soleus muscles, thereby placing a functional overload on the plantaris muscle. As a consequence, there is hypertrophic growth of the plantaris muscle. We used this model to study the molecular mechanisms regulating skeletal muscle hypertrophy. Aged skeletal muscle demonstrates blunted hypertrophic growth in response to functional overload. We hypothesized that an alteration in gene expression would contribute to the blunted hypertrophic response observed with aging. However, the difference in gene expression was modest, with cluster analysis showing a similar pattern of expression between the two groups. Despite ribosomal protein gene expression being higher in the aged group, ribosome biogenesis was significantly lower in aged compared with young skeletal muscle in response to the hypertrophic stimulus (50% versus 2.5-fold, respectively). The failure to fully up-regulate pre-47S ribosomal RNA (rRNA) expression in old skeletal muscle undergoing hypertrophy indicated ribosomal DNA transcription by RNA polymerase I was impaired. Contrary to our hypothesis, the findings of the study suggest that impaired ribosome biogenesis was a primary factor underlying the blunted hypertrophic response observed in old skeletal muscle rather than dramatic differences in gene expression. As it appears ribosomal biogenesis may limit muscle hypertrophy, we assessed the dynamic changes in global transcriptional output during muscle hypertrophy, as the majority of global transcription is dedicated to ribosome biogenesis during periods of rapid growth. Metabolic labeling of nascent RNA using 5-ethynyl uridine permitted the assessment of cell type specific changes in global transcription and how this transcription is distributed within the myofiber. Using this approach, we demonstrate that myofibers are the most transcriptionally active cell-type in skeletal muscle, and furthermore, myonuclei are able to dramatically upregulate global transcription during muscle hypertrophy. Interestingly, the myonuclear accretion that occurs with hypertrophy actually results in lower transcriptional output across nuclei within the muscle fiber relative to sham conditions. These findings argue against the notion that nuclear accretion in skeletal muscle is necessary to increase the transcriptional capacity of the cell in order to support a growth response.

The rapid technological development of high-throughput genomics has given rise to complex high-dimensional microarray datasets. One strategy for reducing the dimensionality of microarray experiments is to carry out a cluster analysis to find groups of genes with similar expression patterns. Though cluster analysis has been studied extensively, the clinical context in which the analysis is performed is usually considered separately if at all. However, allowing clinical outcomes to inform the clustering of microarray data has the potential to identify gene clusters that are more useful for describing the clinical course of disease. The aim of this dissertation is to utilize outcome information to drive the clustering of gene expression data. In Chapter 1, we propose a joint clustering model that assumes a relationship between gene clusters and a continuous patient outcome. Gene expression is modeled using cluster specific random effects such that genes in the same cluster are correlated. A linear combination of these random effects is then used to describe the continuous clinical outcome. We implement a Markov chain Monte Carlo algorithm to iteratively sample the unknown parameters and determine the cluster pattern. Chapter 2 extends this model to binary and failure time outcomes. Our strategy is to augment the data with a latent continuous representation of the outcome and specify that the risk of the event depends on the latent variable. Once the latent variable is sampled, we relate it to gene expression via cluster specific random effects and apply the methods developed in Chapter 1. The setting of clustering longitudinal microarrays using binary and survival outcomes is considered in Chapter 3. We propose a model that incorporates a random intercept and slope to describe the gene expression time trajectory. As before, a continuous latent variable that is linearly related to the random effects is introduced into the model and a Markov chain Monte Carlo algorithm is used for sampling. These methods are applied to microarray data from trauma patients in the Inflammation and Host Response to Injury research project. The resulting partitions are visualized using heat maps that depict the frequency with which genes cluster together.