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Mathematical Modeling of Cell Type E...

Liang, Cong.

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Mathematical Modeling of Cell Type Evolution: Gene Expression and Gene Regulatory Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical Modeling of Cell Type Evolution: Gene Expression and Gene Regulatory Networks./
Author:	Liang, Cong.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	127 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
Subject:	Evolution and Development. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10957329
ISBN:	9780438273740

Mathematical Modeling of Cell Type Evolution: Gene Expression and Gene Regulatory Networks.
Liang, Cong.

Mathematical Modeling of Cell Type Evolution: Gene Expression and Gene Regulatory Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 127 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--Yale University, 2018.

This item must not be added to any third party search indexes.

In evolution, body plan complexity has increased with the number of cell types. To understand the evolution of animal complexity we must first investigate cell type evolution. My research focus on the evolution of cell types by investigating changes in their gene expression and gene regulatory networks. One model for cell type origination is the sister cell type model. According to this model, novel cell types arise with a sister cell type by functional subdivision of ancestral cell types. A key prediction of the sister cell type model is that cell type transcriptomes exhibit tree structure. In chapter 2, we tested this prediction using RNA-seq data from ENCODE and CAGE data from FANTOM project. We found that normal cells have substantial tree structure, which is consistent with the sister cell type model. The result suggests that phylogenetic analysis of cell type transcriptomes can provide mechanistic insights to cell type evolution. The study of cell type evolution is challenging because the functional genomic profiles of cell types are not directly inherited. Cell types from the same species share the same genome, thus one change in the genome may lead to correlated changes in multiple cell types. In chapter 3, we built stochastic models of correlated gene expression evolution. We found that correlated gene expression is pervasive and varies in degrees with the developmental and functional relatedness of tissues under comparison. We demonstrated that hierarchical clustering analysis excluding genes with high correlated evolution signal can correctly recapitulate cell or tissue type history. In chapter 4, we studied the gene regulatory changes in the evolution of decidual stromal cells, a novel cell type in eutherian mammals. By integrating gene expression, open chromatin and the chromatin interaction data, we reconstructed the gene regulatory network that is induced during the decidualization of human endometrial stromal fibroblasts. We compared the response of opossum cells to that of human cells, and found that majority of the induced regulatory network is likely already present in opossum cells. In addition, we identified active enhancer recruitment events that could be experimentally tested in the future.

ISBN: 9780438273740Subjects--Topical Terms:

3422373
Evolution and Development.

Mathematical Modeling of Cell Type Evolution: Gene Expression and Gene Regulatory Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Wagner, Gunter P.
502 $a Thesis (Ph.D.)--Yale University, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a In evolution, body plan complexity has increased with the number of cell types. To understand the evolution of animal complexity we must first investigate cell type evolution. My research focus on the evolution of cell types by investigating changes in their gene expression and gene regulatory networks. One model for cell type origination is the sister cell type model. According to this model, novel cell types arise with a sister cell type by functional subdivision of ancestral cell types. A key prediction of the sister cell type model is that cell type transcriptomes exhibit tree structure. In chapter 2, we tested this prediction using RNA-seq data from ENCODE and CAGE data from FANTOM project. We found that normal cells have substantial tree structure, which is consistent with the sister cell type model. The result suggests that phylogenetic analysis of cell type transcriptomes can provide mechanistic insights to cell type evolution. The study of cell type evolution is challenging because the functional genomic profiles of cell types are not directly inherited. Cell types from the same species share the same genome, thus one change in the genome may lead to correlated changes in multiple cell types. In chapter 3, we built stochastic models of correlated gene expression evolution. We found that correlated gene expression is pervasive and varies in degrees with the developmental and functional relatedness of tissues under comparison. We demonstrated that hierarchical clustering analysis excluding genes with high correlated evolution signal can correctly recapitulate cell or tissue type history. In chapter 4, we studied the gene regulatory changes in the evolution of decidual stromal cells, a novel cell type in eutherian mammals. By integrating gene expression, open chromatin and the chromatin interaction data, we reconstructed the gene regulatory network that is induced during the decidualization of human endometrial stromal fibroblasts. We compared the response of opossum cells to that of human cells, and found that majority of the induced regulatory network is likely already present in opossum cells. In addition, we identified active enhancer recruitment events that could be experimentally tested in the future.
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