Bioinformatic methods were applied to address biological questions. Two new eFP browser web tools were constructed for the intuitive visualization of data from large-scale data sets. In addition, a predicted interactome was constructed for Arabidopsis thaliana and validated using a gene coexpression analysis. The Arabidopsis Interactions Viewer was created to enable access to and visualization of predicted and confirmed interactions in the Arabidopsis interactome. In a separate analysis short sequence matches were identified between introns and coding sequences in several model systems including Arabidopsis, human, C. elegans and 12 Drosophila species. Several hundred to thousands of matches were found near each other in terms of chromosomal location, and were termed Proximal Intron N-mer (PIN) matches. Sequence matches were conserved between 11 Drosophila species and D. melanogaster, suggesting a potential functional role. Novel plasmids were designed to test whether PIN matches are functional in vivo.

Bioinformatic methods were applied to address biological questions. Two new eFP browser web tools were constructed for the intuitive visualization of data from large-scale data sets. In addition, a predicted interactome was constructed for Arabidopsis thaliana and validated using a gene coexpression analysis. The Arabidopsis Interactions Viewer was created to enable access to and visualization of predicted and confirmed interactions in the Arabidopsis interactome. In a separate analysis short sequence matches were identified between introns and coding sequences in several model systems including Arabidopsis, human, C. elegans and 12 Drosophila species. Several hundred to thousands of matches were found near each other in terms of chromosomal location, and were termed Proximal Intron N-mer (PIN) matches. Sequence matches were conserved between 11 Drosophila species and D. melanogaster, suggesting a potential functional role. Novel plasmids were designed to test whether PIN matches are functional in vivo.

Deviation of gene expression from normal levels has been associated with diseases. Both under- and overexpression of genes could lead to deleterious biological consequences. Dosage balance has been proposed to be a key issue of determining gene expression phenotype. Gene deletion or overexpression of any component in a protein complex produces abnormal phenotypes. As a result, interacting partners should be co-expressed to avoid dosage imbalance effects. The strength of transcriptional co-regulation of interacting partners is supposed to reflect gene dosage sensitivity. Although many cases of dosage imbalance effects have been reported, the molecular attributes determining dosage sensitivity remain unknown. This thesis uses a protein structure analysis protocol to explore the molecular basis of gene dosage sensitivity, and studies the post-transcriptional regulation of dosage sensitive genes. Solvent-exposed backbone hydrogen bond (SEBH or called as dehydron) provides a structure marker for protein interaction. Protein structure vulnerability, defined as the ratio of SEBHs to the overall number of backbone hydrogen bonds, quantifies the extent to which protein relies on its binding partners to maintain structure integrity. Genes encoding vulnerable proteins need to be highly co-expressed with their interacting partners. Protein structure vulnerability may hence serves as a structure marker for dosage sensitivity. This hypothesis is examined through the integration of gene expression, protein structure and interaction data sets. Both gene co-expression and protein structure vulnerability are calculated for each interacting subunits from human and yeast complexes. It turns out that structure vulnerability quantifies dosage sensitivity for both temporal phases (yeast) and tissue-specific (human) patterns of mRNA expression, determining the extent of co-expression similarity of binding partners. Highly dosage sensitive genes encode proteins which are vulnerable to water attack. They are subject to tight post-transcriptional regulation. In human, this extra regulation is achieved through extensive microRNA targeting of genes coding for extremely vulnerable proteins. In yeast, on the other hand, our results imply that such a regulation is likely achieved through sequestration of the extremely vulnerable proteins into aggregated states. The 85 genes encoding extremely vulnerable proteins contain the five confirmed yeast prions. It has been proposed that yeast prion protein aggregation could produce multiple phenotypes important for cell survival in some particular circumstances. These results suggest that extremely vulnerable proteins resorting to aggregation to buffer the deleterious consequences of dosage imbalance. However, a rigorous proof will require a structure-based integration of information drawn from the interactome, transcriptome and post-transcriptional regulome.

No description available.

<p>Over the past several decades, many genes have been discovered that govern important functions in the development of a variety of different cancers. However, biological insight from the list of genes is still limited and the underlying mechanisms that occur in the cell during tumorigenesis have not been well established. Studying cancer progression in terms of the oncogenic pathways that are responsible for specific actions that change normal cells into tumors is a means for bringing insight onto these issues. The work presented here will uncover mechanisms that are occurring at the pathway level that first initiate tumor formation and then continue through cancer progression and finally metastasis. This knowledge will allow for drug treatment that is better targeted towards an individual.</p><p>Microarray technology has allowed for the collection of gene expression datasets from clinical cancer and other studies. These datasets can be used to study how expression levels of individual genes or groups of related genes are altered in individuals from different phenotypic groups. Statistical methods exist which assay pathway enrichment by phenotypic class but do not describe individual variation. In order to study this individual variation, we developed a formal statistical method called ASSESS which measures the enrichment of a gene set in each sample in an expression dataset.</p><p>As cancer advances through the stages of initiation, progression, and proliferation, multiple pathways experience disruptions at various times. However, there is still much unknown on these particular pathways that evidence gene expression changes throughout tumorigenesis. Using gene expression datasets comprised of individuals with tumors classified by location and stage, we applied ASSESS in order to study the data on the pathway level. We then utilized novel statistical methods to uncover the pathways that play a role in cancer progression and in what order the pathways become perturbed.</p><p>These analyses can give a basis for how genetic disruptions serve to alter actions in specific cell types. The results may provide insight that will lead to treatments of existing tumors and prevention of incipient cancers from forming. Treatments for existing tumors will use multiple drugs to target the pathways that show an altered state of activity.</p> / Dissertation

<p>Cancer is a complex, multifaceted disease that operates through dynamic changes in the genome. Cancer is best understood through the process that generates it -- random mutations operated on by natural selection -- and several global hallmarks that describe its broad mechanisms. While many genes, protein interactions, and pathways have been enumerated as a kind of ``parts'' list for cancer, researchers are attempting to synthesize broader models for inferring and predicting cancer behavior using high-throughput data and integrative analyses. </p><p>The focus of this thesis is on the development of two novel methods that are optimized for the analysis of complex cancer phenotypes. The first method incorporates ideas from gradient learning with multitask learning to assess statistical dependencies across multiple related data sets. The second method integrates multiscale analysis on graphs and manifolds developed in applied harmonic analysis with sparse factor models, a mainstay of applied statistics. This method generates multiscale factors that are used for inferring hierarchical associations within complex biological networks. The primary biological focus is the inference of gene and pathway dependencies associated with cancer progression and metastatic disease in prostate cancer. Significant findings include evidence of Skp2 degradation of the cell-cycle regulator p27, and the upstream deregulation of the TGF-beta pathway, driving prostate cancer recurrence.</p> / Dissertation

A lot of things happen to proteins when Escherichia coli cells enter stationary phase, such as protein amount, post-translational modifications, conformation changes, and component of protein complex. Proteomics, which study the whole component of proteins, can be used to study the products of the genome and the physiology of Escherichia coli cells at different conditions. By comparing proteome from different growth phases, such as exponential and stationary phase, a lot of proteins with changes can be identified at the same time, which provides a pilot for further studies of mechanism. Current global proteomic studies have identified about 27% of the annotated proteins of E. coli, most of which are predicted to be abundance proteins. Subproteomics, the study of specific subsets of the proteome, can be used to study specific functional classes of proteins and low abundance proteins. In this dissertation, using non-denatured anion exchange column with 2D SDS-PAGE and tandem mass spectrometry, difference of E. coli cells between exponential and stationary phase were studied for whole soluble proteome. Also, using heparin column and mass spectrometry with tandem mass spectrometry, heparin-binding proteins were identified and analyzed for exponential growth and stationary phases. To manage and display the data generated by proteomics, a web-based database has been constructed for experiments in E. coli proteomics (EEP), which includes NonDeLC, Heparome, AIX/2D PAGE and other proteomic studies.

Life sciences research is advancing in breadth and scope, affecting many areas of life including medical care and government policy. The field of Bioinformatics, in particular, is growing very rapidly with the help of computer science, statistics, applied mathematics, and engineering. New high-throughput technologies are making it possible to measure genomic variation across phenotypes in organisms at costs that were once inconceivable. In conjunction, and partly as a consequence, massive amounts of information about the genomes of many organisms are becoming accessible in the public domain. Some of the important and exciting questions in the post-genomics era are how to integrate all of the information available from diverse sources. Learning in complex systems biology requires that information be shared in a natural and interpretable way, to integrate knowledge and data. The statistical sciences can support the advancement of learning in Bioinformatics in many ways, not the least of which is by developing methodologies that can support the synchronization of efforts across sciences, offering real-time learning tools that can be shared across many fields from basic science to the clinical applications. This research is an introduction to several current research problems in Bioinformatics that addresses integration of information, and discusses statistical methodologies from the Bayesian school of thought that may be applied. Bayesian statistical methodologies are proposed to integrate biological knowledge and improve statistical inference for three relevant Bioinformatics applications: gene expression arrays, BAC and aCGH arrays, and real-time gene expression experiments. A unified Bayesian model is proposed to perform detection of genes and gene classes, defined from historical pathways, with gene expression arrays. A novel Bayesian statistical method is proposed to infer chromosomal copy number aberrations in clinical populations with BAC or aCGH experiments. A theoretical model is proposed, motivated from historical work in mathematical biology, for inference with real-time gene expression experiments, and fit with Bayesian methods. Simulation and case studies show that Bayesian methodologies show great promise to improve the way we learn with high-throughput Bioinformatics experiments.

This work is directed towards developing flexible Bayesian statistical methods in the semi- and nonparamteric regression modeling framework with special focus on analyzing data from biological and genetic experiments. This dissertation attempts to solve two such problems in this area. In the first part, we study penalized regression splines (P-splines), which are low-order basis splines with a penalty to avoid under- smoothing. Such P-splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. We model the penalty parameter inherent in the P-spline method as a heteroscedastic regression function. We develop a full Bayesian hierarchical structure to do this and use Markov Chain Monte Carlo tech- niques for drawing random samples from the posterior for inference. We show that the approach achieves very competitive performance as compared to other methods. The second part focuses on modeling DNA microarray data. Microarray technology enables us to monitor the expression levels of thousands of genes simultaneously and hence to obtain a better picture of the interactions between the genes. In order to understand the biological structure underlying these gene interactions, we present a hierarchical nonparametric Bayesian model based on Multivariate Adaptive Regres-sion Splines (MARS) to capture the functional relationship between genes and also between genes and disease status. The novelty of the approach lies in the attempt to capture the complex nonlinear dependencies between the genes which could otherwise be missed by linear approaches. The Bayesian model is flexible enough to identify significant genes of interest as well as model the functional relationships between the genes. The effectiveness of the proposed methodology is illustrated on leukemia and breast cancer datasets.

<p>Guanine nucleotide binding protein (G-protein) coupled receptors (GPCRs), the largest receptor family, is enormously important for the pharmaceutical industry as they are the target of 50-60% of all existing medicines. Discovery of many new GPCR receptors by the “human genome project”, open up new opportunities for developing novel therapeutics. High throughput screening (HTS) of chemical libraries is a well established method for finding new lead compounds in drug discovery. Despite some success this approach has suffered from the near absence of more focused and specific targeted libraries. To improve the hit rates and to maximally exploit the full potential of current corporate screening collections, in this thesis work, identification and analysis of the critical drug-binding positions within the GPCRs were done, based on their overall sequence, their transmembrane regions and their drug binding fingerprints. A proper classification based on drug binding fingerprints on the basis for a successful pharmacophore modelling and virtual screening were done, which facilities in the development of more specific and focused targeted libraries for HTS.</p>