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Fold change detection in 3-node enzymatic networks
Descriptive
TitleInfo
Title
Fold change detection in 3-node enzymatic networks
Name
(type = personal)
NamePart
(type = family)
Škatarić
NamePart
(type = given)
Maja
NamePart
(type = date)
1986-
DisplayForm
Maja Škatarić
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Sontag
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(type = given)
Eduardo
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Eduardo Sontag
Affiliation
Advisory Committee
Role
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(authority = RULIB)
chair
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Orfanidis
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Sophocles
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Sophocles Orfanidis
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
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(type = family)
Spasojevic
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(type = given)
Predrag
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Predrag Spasojevic
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Advisory Committee
Role
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internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
TypeOfResource
Text
Genre
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theses
OriginInfo
DateCreated
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2011
DateOther
(qualifier = exact);
(type = degree)
2011-05
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Complex networks are studied across many fields of science. To discover design principles that underlie these networks, network motifs are introduced, as sub-graphs of interconnections occurring in complex networks much more often than expected at random. A distinct set of network motifs were identified in many types of biological networks, such as gene transcriptional networks, neuronal networks, and enzymatic networks, but only small fraction of them have been well described. By connecting recurrent motifs with a particular cellular function, it is hoped that one can understand the dynamics of the entire network based on the dynamics of its core motifs. Two biologically important functions were introduced and motivated through examples from biology, namely, exact adaptation, which represents a system's ability to respond to a change in the input signal and return to its pre-stimulated state even when the change in input persists, and Fold Change Detection, which is a special property of adapting systems, where the output is invariant under the scaling of inputs. In this thesis, the study of network motifs was used as a motivation to further explore the dynamics of all 3-node enzymatic networks capable of achieving Fold Change Detection property. A search through 16,038 topologies sampled with 10,000 parameters each, led to the conclusion that despite the diversity of enzymatic circuits, only small number of them is capable of achieving the FCD property, and the mechanism for achieving it can be understood through a theoretical and computational analysis.
Subject
(authority = RUETD)
Topic
Electrical and Computer Engineering
Subject
(authority = ETD-LCSH)
Topic
Multienzyme complexes
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Identifier
ETD_3300
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061515
Identifier
(type = doi)
doi:10.7282/T3J67G7Q
PhysicalDescription
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electronic resource
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application/pdf
InternetMediaType
text/xml
Extent
xi, 133 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Maja Škatarić
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Skataric
GivenName
Maja
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2011-04-14 16:06:07
AssociatedEntity
Name
Maja Skataric
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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3716096
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windows xp
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3717120
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