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Modeling and Measuring Dynamic Cellu...

Yao, Jason Chi Hsuan.

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Modeling and Measuring Dynamic Cellular Signaling States.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and Measuring Dynamic Cellular Signaling States./
Author:	Yao, Jason Chi Hsuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	72 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Systematic biology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10278335
ISBN:	9780355119824

Modeling and Measuring Dynamic Cellular Signaling States.
Yao, Jason Chi Hsuan.

Modeling and Measuring Dynamic Cellular Signaling States. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 72 p.

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2017.

The recent progress of single cell quantitative microscopy produced a plethora of data that demonstrated cell to cell variability in signal transduction, which provoked many possible theories that sought to explain the phenomenon of cellular variability. In order to investigate the mechanism of cell to cell variability, mathematical modeling needs to be combined with experimental approach to gain systems-level understanding. However, until now there have been few studies which applied mathematical modeling to study single cell data because of the numerous practical and theoretical hurdles. Here I develop a computational toolbox specifically designed to perform single cell model fitting efficiently on a population scale by utilizing big data platform. The toolbox fits time series data using Bayesian parameter optimization that produces a parameter distribution for the mathematical model. The model fitting is carried out on the Google Cloud Platform which scales well with the increasing number of single cell data and allows for fitting a population of single cell data at fast pace and extreme low cost. I combine the same model fitting algorithm with single cell fluorescent microscopy experiment to study cell to cell variability in calcium signaling pathway. By analyzing the parameter distributions from single cell fitting using clustering methods, I discover distinct clusters within the parameter distributions of cell population that vary the most at IP3 receptor within the calcium signaling pathway. The theoretical findings are corroborated by subsequent experiments which demonstrated that IP3 receptors played significant role in defining calcium response identities in cells.

ISBN: 9780355119824Subjects--Topical Terms:

3173492
Systematic biology.

Modeling and Measuring Dynamic Cellular Signaling States.
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245 1 0 $a Modeling and Measuring Dynamic Cellular Signaling States.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 72 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Roy Wollman.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2017.
520 $a The recent progress of single cell quantitative microscopy produced a plethora of data that demonstrated cell to cell variability in signal transduction, which provoked many possible theories that sought to explain the phenomenon of cellular variability. In order to investigate the mechanism of cell to cell variability, mathematical modeling needs to be combined with experimental approach to gain systems-level understanding. However, until now there have been few studies which applied mathematical modeling to study single cell data because of the numerous practical and theoretical hurdles. Here I develop a computational toolbox specifically designed to perform single cell model fitting efficiently on a population scale by utilizing big data platform. The toolbox fits time series data using Bayesian parameter optimization that produces a parameter distribution for the mathematical model. The model fitting is carried out on the Google Cloud Platform which scales well with the increasing number of single cell data and allows for fitting a population of single cell data at fast pace and extreme low cost. I combine the same model fitting algorithm with single cell fluorescent microscopy experiment to study cell to cell variability in calcium signaling pathway. By analyzing the parameter distributions from single cell fitting using clustering methods, I discover distinct clusters within the parameter distributions of cell population that vary the most at IP3 receptor within the calcium signaling pathway. The theoretical findings are corroborated by subsequent experiments which demonstrated that IP3 receptors played significant role in defining calcium response identities in cells.
590 $a School code: 0033.
650 4 $a Systematic biology. $3 3173492
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710 2 $a University of California, San Diego. $b Bioinformatics and Systems Biology. $3 3285993
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