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Mathematical modeling of signaling p...

Wang, Xiao.

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Mathematical modeling of signaling pathway dynamics and stochastic gene expression.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mathematical modeling of signaling pathway dynamics and stochastic gene expression./
Author:	Wang, Xiao.
Description:	113 p.
Notes:	Adviser: Timothy C. Elston.
Contained By:	Dissertation Abstracts International67-05B.
Subject:	Health Sciences, Pharmacology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3219438
ISBN:	9780542689352

Mathematical modeling of signaling pathway dynamics and stochastic gene expression.
Wang, Xiao.

Mathematical modeling of signaling pathway dynamics and stochastic gene expression. - 113 p.

Adviser: Timothy C. Elston.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.

This thesis presents the development and analysis of stochastic and deterministic models of cellular biochemical networks, such as signaling pathways and gene regulatory networks. First, the model of the yeast pheromone response pathway is constructed. Stochastic modeling reveals that the biochemical steps that regulate activation of the mitogen-activated protein kinase Fus3 can account for the graded-to-binary conversion. The model is also used to investigate the effects of protein turnover on the response of the pathway. It is demonstrated that the inclusion of protein turnover can lead to sustained oscillations of protein concentration in the absence of feedback regulation, which indicates protein turnover as an important signaling regulation mechanism. Second, an engineered promoter that allowed the simultaneous repression and activation of gene expression in Escherichia coli was constructed and used to construct a stochastic model to study synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated and simultaneously repressed and activated, and in the presence of positive feedback. The stochastic model quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system and accurately predict the in vivo behavior of an expanded network that includes positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell division, which was confirmed experimentally.

ISBN: 9780542689352Subjects--Topical Terms:

1017717
Health Sciences, Pharmacology.

Mathematical modeling of signaling pathway dynamics and stochastic gene expression.
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100 1 $a Wang, Xiao. $3 1292882
245 1 0 $a Mathematical modeling of signaling pathway dynamics and stochastic gene expression.
300 $a 113 p.
500 $a Adviser: Timothy C. Elston.
500 $a Source: Dissertation Abstracts International, Volume: 67-05, Section: B, page: 2815.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.
520 $a This thesis presents the development and analysis of stochastic and deterministic models of cellular biochemical networks, such as signaling pathways and gene regulatory networks. First, the model of the yeast pheromone response pathway is constructed. Stochastic modeling reveals that the biochemical steps that regulate activation of the mitogen-activated protein kinase Fus3 can account for the graded-to-binary conversion. The model is also used to investigate the effects of protein turnover on the response of the pathway. It is demonstrated that the inclusion of protein turnover can lead to sustained oscillations of protein concentration in the absence of feedback regulation, which indicates protein turnover as an important signaling regulation mechanism. Second, an engineered promoter that allowed the simultaneous repression and activation of gene expression in Escherichia coli was constructed and used to construct a stochastic model to study synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated and simultaneously repressed and activated, and in the presence of positive feedback. The stochastic model quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system and accurately predict the in vivo behavior of an expanded network that includes positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell division, which was confirmed experimentally.
590 $a School code: 0153.
650 4 $a Health Sciences, Pharmacology. $3 1017717
650 4 $a Mathematics. $3 515831
650 4 $a Operations Research. $3 626629
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710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 67-05B.
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790 1 0 $a Elston, Timothy C., $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3219438