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Modeling correlated mutations in computational biology using log-linear analysis and graph-theoretic probabilistic inference methods

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TitleInfo
Title
Modeling correlated mutations in computational biology using log-linear analysis and graph-theoretic probabilistic inference methods
Name (type = personal)
NamePart (type = family)
Haq
NamePart (type = given)
Omar
DisplayForm
Omar Haq
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Levy
NamePart (type = given)
Ronald M
DisplayForm
Ronald M Levy
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Morozov
NamePart (type = given)
Alexander
DisplayForm
Alexander Morozov
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Gallicchio
NamePart (type = given)
Emilio
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Emilio Gallicchio
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Bromberg
NamePart (type = given)
Yana
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Yana Bromberg
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2012
DateOther (qualifier = exact); (type = degree)
2012-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Point mutations are random events but selection for protein stability and function fixes specific combinations of amino acid mutations in the protein population. Many mutations are not independent but are found to be strongly correlated, the signal for which is present in multiple sequence alignment data. Using HIV Protease as a model system, this work bridges the gap between the analysis of protein sequences using statistical techniques developed by the physics and computer science communities, and the biophysical modeling of protein energetics. Using information theoretic methods together with a coarse-grained (Generalized Born) energy model we have analyzed the contribution of electrostatic interactions to protein stability among mutated residues of HIV-1 protease based on models derived from a large database of sequences which have acquired drug resistance. In the course of this work we have constructed a mean field model at the level of pair correlations (Bethe approximation) to predict the probabilities of observing mutated sequences using the HIV sequence database to parameterize the model.
Subject (authority = RUETD)
Topic
Computational Biology and Molecular Biophysics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4226
PhysicalDescription
Form (authority = gmd)
electronic resource
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application/pdf
InternetMediaType
text/xml
Extent
xxix, 292 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Omar Haq
Subject (authority = ETD-LCSH)
Topic
Amino acid sequence
Subject (authority = ETD-LCSH)
Topic
Computational biology
Subject (authority = ETD-LCSH)
Topic
HIV (Viruses)
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066759
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T38G8JHS
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Haq
GivenName
Omar
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2012-09-10 22:56:16
AssociatedEntity
Name
Omar Haq
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2012-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2014-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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