Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 138-157). Also available in electronic version.

Thesis (Ph.D.) -- McMaster University, 2001. / Includes bibliographical references (leaves 177-219). Also available via World Wide Web.

Thesis (Ph. D. in Cell and Developmental Biology)--Vanderbilt University, Aug. 2007. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 297-327).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 135-144). Available also in a digital version from Dissertation Abstracts.

No description available.

DNA microarray technology has been extensively utilized in the biomedical field, becoming a standard in identifying gene expression signatures for disease diagnosis/prognosis and pharmaceutical practices. Although cancer research has benefited from this technology, challenges such as large-scale data size, few replicates and complex heterogeneous data types remain; thus the biomarkers identified by various studies have a small proportion of overlap because of molecular heterogeneity. However, it is desirable in cancer research to consider robust and consistent biomarkers for drug development as well as diagnosis/prognosis. Although cancer is a highly heterogeneous disease, some mechanism common to developing cancers is believed to exist; integrating datasets from multiple experiments increases the accuracy of predictions because increasing the sample size improves and enhances biomarkers detection. Therefore, integrative study is required for compiling multiple cancer data sets when searching for the common mechanism leading to cancers. Some critical challenges of integration analysis remain despite many successful methods introduced. Few is able to work on data sets with different dimensionalities. More seriously, when the replicate number is small, most existing algorithms cannot deliver robust predictions through an integrative study. In fact, as modern high-throughput technology matures to provide increasingly precise data, and with well-designed experiments, variance across replicates is believed to be small for us to consider a mean pattern model. This model assumes that all the genes (or metabolites, proteins or DNA copies) are random samples of a hidden (mean pattern) model. The study implements this model using a hierarchical modelling structure. As the primary component of the system, a multi-scale Gaussian (MSG) model, designed to identify robust differentially-expressed genes to be integrated, was developed for predicting differentially expressed genes from microarray expression data of small replicate numbers. To assure the validity of the mean pattern hypothesis, a bimodality detection method that was a revision of the Bimodality index was proposed.

No description available.

Conditional gene expression systems that enable inducible and reversible transcriptional control are essential research tools and have broad applications in biomedicine and biotechnology. The reverse tetracycline transcriptional activator is a canonical system for engineered gene expression control that enables graded and gratuitous modulation of target gene transcription in eukaryotes from yeast to human cell lines and transgenic animals. However, the system has a tendency to activate transcription even in the absence of tetracycline and this leaky target gene expression impedes its use. Here, we identify single amino acid substitutions that greatly enhance the dynamic range of the system by reducing leaky transcription to undetectable levels while retaining high expression capacity in the presence of inducer. Furthermore, we show that these improved DNA binding domains can be fused to repression domains to create synthetic transcriptional repressors. The function of these transcriptional repressors is dependent on the location of their recruitment and their mechanisms of action.