RUcore: Rutgers University Community Repository
Inferring models and structure from biological data: networks and pathways
Descriptive
TitleInfo
Title
Inferring models and structure from biological data: networks and pathways
Name
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NamePart
(type = family)
Putnins
NamePart
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Matthew
NamePart
(type = date)
1990-
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Matthew Putnins
Role
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author
Name
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NamePart
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Androulakis
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Ioannis
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Ioannis Androulakis
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Advisory Committee
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chair
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Cai
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Li
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Li Cai
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Advisory Committee
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(authority = RULIB)
internal member
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(type = family)
Shinbrot
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Troy
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Troy Shinbrot
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
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(type = family)
Axelrod
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David
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David Axelrod
Affiliation
Advisory Committee
Role
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outside member
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Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Graduate Studies
Role
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school
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Text
Genre
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theses
Genre
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ETD doctoral
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2020
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2020-10
Language
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English
Abstract
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Physiological functions are driven by the emergent behaviors of many individual components, whether they are gene, protein, or metabolic interactions. These interactions form biochemical pathways and interaction networks which then lead to more complex cellular or organismal level behaviors that are not knowable from the characteristics of an individual component of that system. Knowing whether a gene is being expressed or knowing the structure of a protein does not necessarily imply the physiological function of either, but within the context of a meaningful biological system, we can infer more complex behaviors. In the enclosed dissertation we present multiple approaches to contextualize biological components into more complex systems. These methods include utilizing Boolean networks to model interactions in a qualitative manner, as well as analyzing expression data in the context of biochemical pathways. We use two distinct approaches for understanding biological systems: We utilize evolutionary algorithms to understand the origin and development of complex systems. This evolutionary framework enables a better understanding of complex network structures as well as evolutionary strategies used in the development of complex biological systems.
We additionally propose a data-driven approach for interrogating gene expression within the context of biochemical pathways. We utilize a novel method for detecting circadian genes and map these genes onto physiologically functional pathways. We utilize this data to validate methods for constructing a Boolean network to infer the causal relationships which exist within gene pathways. This analysis will improve the applications of high throughput data analysis for the purpose of identifying critical components of complex biological systems.
We additionally propose a data-driven approach for interrogating gene expression within the context of biochemical pathways. We utilize a novel method for detecting circadian genes and map these genes onto physiologically functional pathways. We utilize this data to validate methods for constructing a Boolean network to infer the causal relationships which exist within gene pathways. This analysis will improve the applications of high throughput data analysis for the purpose of identifying critical components of complex biological systems.
Subject
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Topic
Evolution
Subject
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Topic
Biomedical Engineering
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_11162
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application/pdf
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text/xml
Extent
1 online resource (xi, 103 pages) : illustrations
Note
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Ph.D.
Note
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Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-rnnm-8y04
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Putnins
GivenName
Matthew
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2020-09-18 13:35:01
AssociatedEntity
Name
Matthew Putnins
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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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2020-09-10T12:55:09
DateCreated
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2020-09-10T12:55:09
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