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Structural and dynamic analysis of b...

Mohyedin Bonab, Elmira.

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Structural and dynamic analysis of biological networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Structural and dynamic analysis of biological networks./
Author:	Mohyedin Bonab, Elmira.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	196 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3702343
ISBN:	9781321735970

Structural and dynamic analysis of biological networks.
Mohyedin Bonab, Elmira.

Structural and dynamic analysis of biological networks. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 196 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2015.

Network analytic provides a systematic approach in revealing the complexity of biological processes. Consequently, the statistical and structural properties of these biological networks are quantitatively comparable with other well-studied networks such as social networks. We constructed three application-dependent biological networks: a gene regulatory network, signaling network and cold-stimulated white adipose gene network. The gene regulatory network is constructed using correlations between temporal gene expression profiles along with molecular binding information. The signaling network is built using human signaling transduction pathways. In the latter network, differentially expressed cold-induced genes are connected as they participate in similar biological processes and have significantly correlated expression profiles. Information spreading patterns or activation patterns in a biological network reveals network modularity features. There is a dependency between where the flow is originated and where is traveling in molecular activation patterns. Therefore, we compared the ability of a random walker, as the simplest information spreading modeler, with second-order Markov dynamics in biological networks. As compared to first-order Markov dynamics (random walker), second-order Markov model can better identify connections between chains of biological processes in signaling and white adipose tissue networks, such that detected modules by second-order Markov dynamics have distinctive, specific and densely interrelated biological processes.

ISBN: 9781321735970Subjects--Topical Terms:

553671
Bioinformatics.

Structural and dynamic analysis of biological networks.
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020 $a 9781321735970
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100 1 $a Mohyedin Bonab, Elmira. $3 3283084
245 1 0 $a Structural and dynamic analysis of biological networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 196 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Advisers: Mo Jamshidi; Yu-Fang Jin.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2015.
520 $a Network analytic provides a systematic approach in revealing the complexity of biological processes. Consequently, the statistical and structural properties of these biological networks are quantitatively comparable with other well-studied networks such as social networks. We constructed three application-dependent biological networks: a gene regulatory network, signaling network and cold-stimulated white adipose gene network. The gene regulatory network is constructed using correlations between temporal gene expression profiles along with molecular binding information. The signaling network is built using human signaling transduction pathways. In the latter network, differentially expressed cold-induced genes are connected as they participate in similar biological processes and have significantly correlated expression profiles. Information spreading patterns or activation patterns in a biological network reveals network modularity features. There is a dependency between where the flow is originated and where is traveling in molecular activation patterns. Therefore, we compared the ability of a random walker, as the simplest information spreading modeler, with second-order Markov dynamics in biological networks. As compared to first-order Markov dynamics (random walker), second-order Markov model can better identify connections between chains of biological processes in signaling and white adipose tissue networks, such that detected modules by second-order Markov dynamics have distinctive, specific and densely interrelated biological processes.
590 $a School code: 1283.
650 4 $a Bioinformatics. $3 553671
650 4 $a Systematic biology. $3 3173492
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710 2 $a The University of Texas at San Antonio. $b Electrical and Computer Engineering. $3 2104320
773 0 $t Dissertation Abstracts International $g 76-09B(E).
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792 $a 2015
793 $a English
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