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Computational inference of transcrip...

Haverty, Peter Michael.

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Computational inference of transcriptional regulatory networks in eukaryotes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational inference of transcriptional regulatory networks in eukaryotes./
Author:	Haverty, Peter Michael.
Description:	183 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3808.
Contained By:	Dissertation Abstracts International65-08B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3142377
ISBN:	049600199X

Computational inference of transcriptional regulatory networks in eukaryotes.
Haverty, Peter Michael.

Computational inference of transcriptional regulatory networks in eukaryotes. - 183 p.

Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3808.

Thesis (Ph.D.)--Boston University, 2005.

This work takes an integrative approach to a key problem in Systems Biology: the determination of transcriptional regulatory networks. Transcriptional regulatory networks describe regulation of changes in gene expression occurring in response to specific changes in a cell's environment. Potential changes include the introduction of extracellular molecular signals, such as hormones or pharmaceuticals, or a change in the available nutrients. Such extracellular signals result in changes in the activities of transcription factors, which regulate the expression of specific genes when bound to associated sites in the genome.

ISBN: 049600199XSubjects--Topical Terms:

1018416
Biology, Biostatistics.

Computational inference of transcriptional regulatory networks in eukaryotes.
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035 $a (UnM)AAI3142377
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040 $a UnM $c UnM
100 1 $a Haverty, Peter Michael. $3 1935508
245 1 0 $a Computational inference of transcriptional regulatory networks in eukaryotes.
300 $a 183 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3808.
500 $a Major Professor: Zhiping Weng.
502 $a Thesis (Ph.D.)--Boston University, 2005.
520 $a This work takes an integrative approach to a key problem in Systems Biology: the determination of transcriptional regulatory networks. Transcriptional regulatory networks describe regulation of changes in gene expression occurring in response to specific changes in a cell's environment. Potential changes include the introduction of extracellular molecular signals, such as hormones or pharmaceuticals, or a change in the available nutrients. Such extracellular signals result in changes in the activities of transcription factors, which regulate the expression of specific genes when bound to associated sites in the genome.
520 $a I present a computational toolset for inferring transcriptional networks from genome sequence and global gene expression data. This system, CARRIE, uses gene expression profiling to identify genes that respond to an experimental stimulus. Transcription factors that regulate these genes are identified using: (1) expression level changes for genes encoding transcription factors, and (2) relative binding site overabundance in regulatory regions of affected genes. Specific binding sites are evaluated to determine which of the selected genes are regulated by each selected transcription factor. The stimulatory or inhibitory effects of transcription factors are inferred from expression changes in target and transcription factor genes.
520 $a This dissertation details the development, testing, and successful application of CARRIE. An initial study of a simple eukaryote, the budding yeast Saccharomyces cerevisiae, demonstrates that CARRIE accurately and effectively predicts the transcription factors that regulate cellular responses and the specific genes that respond directly to these transcription factors. I further test the system in the setting of a complex mammalian tissue by inferring a transcriptional regulatory network controlled by the adenosine receptor A2A in the mouse striatum. These findings may be relevant to the treatment of Parkinson's disease. Finally, CARRIE is applied to a highly defined mammalian system in an analysis of global gene expression changes in a human glioblastoma cell line immediately following stimulation by platelet-derived growth factor. CARRIE rediscovers documented regulatory interactions and suggests important molecular mechanisms for regulating growth through two central growth stimulatory cellular signaling pathways. Overall, these findings demonstrate the ability of CARRIE to discover complex regulatory mechanisms and provide testable hypotheses to drive future research.
590 $a School code: 0017.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Molecular. $3 1017719
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690 $a 0307
710 2 0 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 65-08B.
790 1 0 $a Weng, Zhiping, $e advisor
790 $a 0017
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3142377