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Mechanisms and models of gene regula...

Kim, Sohyoung.

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Mechanisms and models of gene regulation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mechanisms and models of gene regulation./
Author:	Kim, Sohyoung.
Description:	159 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2246.
Contained By:	Dissertation Abstracts International65-05B.
Subject:	Biology, Molecular. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3134088
ISBN:	0496813242

Mechanisms and models of gene regulation.
Kim, Sohyoung.

Mechanisms and models of gene regulation. - 159 p.

Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2246.

Thesis (Ph.D.)--George Mason University, 2004.

The central goal in the post-genomic era is the elucidation of the regulatory relationships among all cellular constituents establishing biological networks of a cell. Recent innovations in high-throughput techniques have provided a rich set of genome-wide data, which allows us to examine gene regulation at several levels, from detailed molecular mechanisms to the collective phenomenon of molecular interactions. By focusing on two types of high-throughput data, this work addresses both detailed molecular mechanisms and theoretical models of gene regulation. Three specific topics are investigated. First, patterns of DNA methylation and gene regulation in relation to DNA methylation were investigated, using a new software tool called MethMiner. MethMiner is applied to understand gene regulation of a cancer-related gene, illustrating an approach that is expected to help us to comprehend molecular foundations of cancer. The MethMiner developed in this study has wide applications beyond DNA methylation in cancer, including the areas of aging, animal development, and gene imprinting. The second topic addressed here is the modeling of gene regulations, based on large-scale molecular data, in terms of network structures and dynamics. New methods for inferring the structure of gene regulation networks are evaluated on simulated data and applied to yeast microarray data. The potential applications of such methods include comparing on cancer and normal cells to provide an understanding of differences between the cells, and such understanding may suggest a potential new paradigm for diagnostic tools. We addressed the dynamics of models of gene regulation by proposing a new class of biologically plausible Boolean function to model gene interactions. Analyses of the dynamics of the resulting networks show that the proposed new functions combined with scale free topology may provide more plausible biological models than random Boolean functions with exponential topology. This study is a demonstration of the synergistic relationships between experimental and theoretical studies. As a final topic, this work evaluates a common assumption concerning transcriptional co-regularity of biologically relevant molecules, on which current pathway inference methods are based. The results imply that predictions based on expression similarity or anti-similarity are generally insufficient to uncover individual interactions with high confidence.

ISBN: 0496813242Subjects--Topical Terms:

1017719
Biology, Molecular.

Mechanisms and models of gene regulation.
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502 $a Thesis (Ph.D.)--George Mason University, 2004.
520 $a The central goal in the post-genomic era is the elucidation of the regulatory relationships among all cellular constituents establishing biological networks of a cell. Recent innovations in high-throughput techniques have provided a rich set of genome-wide data, which allows us to examine gene regulation at several levels, from detailed molecular mechanisms to the collective phenomenon of molecular interactions. By focusing on two types of high-throughput data, this work addresses both detailed molecular mechanisms and theoretical models of gene regulation. Three specific topics are investigated. First, patterns of DNA methylation and gene regulation in relation to DNA methylation were investigated, using a new software tool called MethMiner. MethMiner is applied to understand gene regulation of a cancer-related gene, illustrating an approach that is expected to help us to comprehend molecular foundations of cancer. The MethMiner developed in this study has wide applications beyond DNA methylation in cancer, including the areas of aging, animal development, and gene imprinting. The second topic addressed here is the modeling of gene regulations, based on large-scale molecular data, in terms of network structures and dynamics. New methods for inferring the structure of gene regulation networks are evaluated on simulated data and applied to yeast microarray data. The potential applications of such methods include comparing on cancer and normal cells to provide an understanding of differences between the cells, and such understanding may suggest a potential new paradigm for diagnostic tools. We addressed the dynamics of models of gene regulation by proposing a new class of biologically plausible Boolean function to model gene interactions. Analyses of the dynamics of the resulting networks show that the proposed new functions combined with scale free topology may provide more plausible biological models than random Boolean functions with exponential topology. This study is a demonstration of the synergistic relationships between experimental and theoretical studies. As a final topic, this work evaluates a common assumption concerning transcriptional co-regularity of biologically relevant molecules, on which current pathway inference methods are based. The results imply that predictions based on expression similarity or anti-similarity are generally insufficient to uncover individual interactions with high confidence.
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