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Modeling p53 transcriptional regulation
Descriptive
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Text
TitleInfo
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Title
Modeling p53 transcriptional regulation
Identifier
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17551
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ETD_1134
Language
LanguageTerm
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English
Genre
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theses
Subject
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Topic
Computational Biology and Molecular Biophysics
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
p53 antioncogene
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Genetic transcription
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Transcription factors
Abstract
The p53 protein has been called the "gatekeeper" of the cell. After DNA damage, p53 transcriptionally activates downstream pathways to prevent cancer from occurring. Target genes are activated to cause cell cycle arrest, DNA repair, cell senescence, and apoptosis. However, the exact transcriptional program that determines the specific outcome of a certain cell stress is not completely understood. We present an analysis to help shed light on the mechanisms of transcriptional regulation by the p53 protein.
First, we present a detailed analysis of the known modes of p53-regulation, and a dataset of 160 functional human p53-binding sites curated from the literature. Second, we present a new method (called p53HMM) to model p53-binding sites using Profile Hidden Markov Models (PHMMs). This new method is the most accurate predictor of functional p53 singlesites and cluster-sites to date. Third, we show that functional sites with low estimated relative affnity scores are highly correlated with distances from the TSS. Fourth, we show that the capability to fold into a non-linear cruciform DNA structure is an important predictor in estimating the overall binding affinity of a functional p53-binding site. We use UNAFold to calculate free energies and probabilities of p53-binding sites folding into different non-linear cruciform structures. Fifth, we present a new motif-finding algorithm (called PURE) that uses relative entropy to find over- and under-represented motifs near functional p53-binding sites. The goal is to find possible motifs (like co-factor motifs) that can help designate functional p53-binding sites, thereby reducing the false positive rate that currently plagues motif-finding algorithms.
First, we present a detailed analysis of the known modes of p53-regulation, and a dataset of 160 functional human p53-binding sites curated from the literature. Second, we present a new method (called p53HMM) to model p53-binding sites using Profile Hidden Markov Models (PHMMs). This new method is the most accurate predictor of functional p53 singlesites and cluster-sites to date. Third, we show that functional sites with low estimated relative affnity scores are highly correlated with distances from the TSS. Fourth, we show that the capability to fold into a non-linear cruciform DNA structure is an important predictor in estimating the overall binding affinity of a functional p53-binding site. We use UNAFold to calculate free energies and probabilities of p53-binding sites folding into different non-linear cruciform structures. Fifth, we present a new motif-finding algorithm (called PURE) that uses relative entropy to find over- and under-represented motifs near functional p53-binding sites. The goal is to find possible motifs (like co-factor motifs) that can help designate functional p53-binding sites, thereby reducing the false positive rate that currently plagues motif-finding algorithms.
PhysicalDescription
Extent
viii, 180 pages
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Note
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Ph.D.
Note
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Includes bibliographical references (p. 157-179).
Name
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Riley
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Todd Robert
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author
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Todd Robert Riley
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Levine
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Arnold
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chair
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Arnold J. Levine
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Sontag
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Eduardo
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co-chair
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Advisory Committee
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Eduardo D. Sontag
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(type = family)
Bhanot
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Gyan
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internal member
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Advisory Committee
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Gyan Bhanot
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NamePart
(type = family)
Stolovitsky
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Gustavo
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outside member
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Advisory Committee
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Gustavo A. Stolovitsky
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NamePart
Rutgers University
Role
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degree grantor
Name
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Graduate School - New Brunswick
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school
OriginInfo
DateCreated
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2008
DateOther
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2008-10
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NjNbRU
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Identifier
(type = doi)
doi:10.7282/T31836RN
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
Status
Open
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Name
Todd Riley
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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