國立虎尾科技大學 |

Data-Driven Models for Dynamics of Gene Expression and Single Cells.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Models for Dynamics of Gene Expression and Single Cells./
作者:	Peng, Tao.
面頁冊數:	1 online resource (146 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355308167

Data-Driven Models for Dynamics of Gene Expression and Single Cells.
Peng, Tao.

Data-Driven Models for Dynamics of Gene Expression and Single Cells. - 1 online resource (146 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This thesis uses mathematical models to study the dynamics of biological systems under the single cell level. In the first chapter we study a minimal gene regulatory network permissive of multi-lineage mesenchymal stem cell differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. In the second chapter we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method, a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. In the third chapter on the basis of the functional mapping assays of primary visual cortex, we conducted a quantitative assessment of both excitatory and inhibitory synaptic laminar connections to excitatory cells at single cell resolution, establishing precise layer-by-layer synaptic wiring diagrams of excitatory and inhibitory neurons in the visual cortex inferred by the mathematical model. In the fourth chapter we constructed a multi-scale mathematical model integrating the gene regulatory network and cell lineage to study the functions of key genes in controlling mouse embryonic epidermis development. In the fifth chapter we studied the selections of models when prior information is provided to infer the gene regulatory network combining the expression data and ChIP-seq data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355308167Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Data-Driven Models for Dynamics of Gene Expression and Single Cells.
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500 $a Adviser: Qing Nie.
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504 $a Includes bibliographical references
520 $a This thesis uses mathematical models to study the dynamics of biological systems under the single cell level. In the first chapter we study a minimal gene regulatory network permissive of multi-lineage mesenchymal stem cell differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. In the second chapter we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method, a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. In the third chapter on the basis of the functional mapping assays of primary visual cortex, we conducted a quantitative assessment of both excitatory and inhibitory synaptic laminar connections to excitatory cells at single cell resolution, establishing precise layer-by-layer synaptic wiring diagrams of excitatory and inhibitory neurons in the visual cortex inferred by the mathematical model. In the fourth chapter we constructed a multi-scale mathematical model integrating the gene regulatory network and cell lineage to study the functions of key genes in controlling mouse embryonic epidermis development. In the fifth chapter we studied the selections of models when prior information is provided to infer the gene regulatory network combining the expression data and ChIP-seq data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
690 $a 0405
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710 2 $a University of California, Irvine. $b Mathematics - Ph.D.. $3 1180567
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