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Computational Modeling of Immune Cel...

Signoriello, Alexandra Rae.

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Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model./
Author:	Signoriello, Alexandra Rae.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	106 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-01, Section: B.
Contained By:	Dissertations Abstracts International81-01B.
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13918039
ISBN:	9781392328699

Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model.
Signoriello, Alexandra Rae.

Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 106 p.

Source: Dissertations Abstracts International, Volume: 81-01, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

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

I present an inter-disciplinary project that merges image analysis and physical modeling to study the structural (size and shape) and mechanical (packing geometry) properties of cells in melanoma tumor development. I describe a new model, the Deformable Particle Model (DPM), to study the material properties for cells. The model is used to study the packing geometry of cell monolayers as a function of the flexibility of the cell. The model can be adapted to study cells for a broad range of sizes, shapes, stiffness, and activity. I also developed a cell segmentation, identification and tracking software package for high-resolution microscope images, and shape analysis results for tumor associated macrophages in murine melanoma tumors. The tools developed in this project improve the extraction of quantitative information from microscopy images, which are generated in large numbers in the field of biology. The automatic detection of cells in vivoreduces the time spent manually counting cells and drawing boundaries, and improves the accuracy of identifcation and size calculations. The data generated from the images are analyzed to classify cell populations, observe spatial correlations, and detect cell behavior. Image analysis results will be presented for macrophages inside murine melanoma tumors. The results support the hypothesis that macrophages respond to the mechanical environment, and alter their function based on physical interactions.

ISBN: 9781392328699Subjects--Topical Terms:

553671
Bioinformatics.

Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model.
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245 1 0 $a Computational Modeling of Immune Cells during Tumor Development using the Deformable Particle Model.
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500 $a Source: Dissertations Abstracts International, Volume: 81-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: O'Hern, Corey S.;Bosenberg, Marcus W.
502 $a Thesis (Ph.D.)--Yale University, 2019.
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.
520 $a I present an inter-disciplinary project that merges image analysis and physical modeling to study the structural (size and shape) and mechanical (packing geometry) properties of cells in melanoma tumor development. I describe a new model, the Deformable Particle Model (DPM), to study the material properties for cells. The model is used to study the packing geometry of cell monolayers as a function of the flexibility of the cell. The model can be adapted to study cells for a broad range of sizes, shapes, stiffness, and activity. I also developed a cell segmentation, identification and tracking software package for high-resolution microscope images, and shape analysis results for tumor associated macrophages in murine melanoma tumors. The tools developed in this project improve the extraction of quantitative information from microscopy images, which are generated in large numbers in the field of biology. The automatic detection of cells in vivoreduces the time spent manually counting cells and drawing boundaries, and improves the accuracy of identifcation and size calculations. The data generated from the images are analyzed to classify cell populations, observe spatial correlations, and detect cell behavior. Image analysis results will be presented for macrophages inside murine melanoma tumors. The results support the hypothesis that macrophages respond to the mechanical environment, and alter their function based on physical interactions.
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