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Advances in the prediction and under...

Reddy, Timothy Edward.

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Advances in the prediction and understanding of transcriptional regulation in yeast.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advances in the prediction and understanding of transcriptional regulation in yeast./
Author:	Reddy, Timothy Edward.
Description:	158 p.
Notes:	Adviser: Charles DeLisi.
Contained By:	Dissertation Abstracts International69-01B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296032
ISBN:	9780549412861

Advances in the prediction and understanding of transcriptional regulation in yeast.
Reddy, Timothy Edward.

Advances in the prediction and understanding of transcriptional regulation in yeast. - 158 p.

Adviser: Charles DeLisi.

Thesis (Ph.D.)--Boston University, 2008.

Transcriptional regulation is at the very foundation of molecular biology. Governing the regulation is a complex code found in regions of the genome between the genes and much effort has gone into the study of the regulatory code. My work builds in depth and breadth by developing novel computational approaches to study known regulatory mechanisms, as well as by exploring unanswered questions about the function of genomic regulatory signals.

ISBN: 9780549412861Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Advances in the prediction and understanding of transcriptional regulation in yeast.
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020 $a 9780549412861
035 $a (UMI)AAI3296032
035 $a AAI3296032
040 $a UMI $c UMI
100 1 $a Reddy, Timothy Edward. $3 1263114
245 1 0 $a Advances in the prediction and understanding of transcriptional regulation in yeast.
300 $a 158 p.
500 $a Adviser: Charles DeLisi.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0079.
502 $a Thesis (Ph.D.)--Boston University, 2008.
520 $a Transcriptional regulation is at the very foundation of molecular biology. Governing the regulation is a complex code found in regions of the genome between the genes and much effort has gone into the study of the regulatory code. My work builds in depth and breadth by developing novel computational approaches to study known regulatory mechanisms, as well as by exploring unanswered questions about the function of genomic regulatory signals.
520 $a Transcriptional regulation is carried out by a class of proteins known as transcription factors (TFs). TFs interact with regulatory sequences in the DNA and, in doing so, regulate transcription. Identification of the TF-bound regulatory sequences has long challenged computational biology. Here, I took a novel approach to study the behavior of a commonly used regulatory motif detection algorithm, Gibbs sampling. Based on the study, a series of new motif detection algorithms are developed that utilize high performance computing to better predict regulatory sequences.
520 $a At the core of the developed algorithms is the concept that statistical sampling procedures are improved by observing the behavior of repeated application to the same dataset. Here, such ensemble approaches are used to identify regulatory sequences involved in a mammalian model of epilepsy. Additionally, combining the ensemble approach with graph theory significantly improves upon the ability of existing algorithms to predict yeast regulatory signals, and this work presents what is, to the best of my knowledge, the first application of graphically clustering Gibbs sampling results to predict regulatory sequences across a eukaryotic genome.
520 $a It is well known that the function of regulatory sequences is modulated by aspects of the promoter beyond the binding site. However, current computational approaches do not consider aspects of the promoter outside the specific regulatory sequence thus limiting the utility of computational predictions. To address the problem, this work concludes with a case study of the role of promoter architecture in modulating the function of local regulatory sequences. The global sequence landscape appears to play a major and evolutionarily conserved role in modulating the function of otherwise well-studied local regulatory signals. The result suggests that new approaches to combine local and global sequence properties will better predict regulatory functions.
590 $a School code: 0017.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0308
690 $a 0369
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710 2 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 69-01B.
790 $a 0017
790 1 0 $a DeLisi, Charles, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296032