Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division

<p>Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, <italic>Saccharomyces cerevisiae</italic>, these two processes are known to be coordinated with one another as a cell's mass must roughly double before division. Moreov...

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Main Author: Mayhew, Michael Benjamin
Other Authors: Hartemink, Alexander J
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10161/9089
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spelling ndltd-DUKE-oai-dukespace.lib.duke.edu-10161-90892015-02-24T03:32:23ZComputational Systems Biology of Saccharomyces cerevisiae Cell Growth and DivisionMayhew, Michael BenjaminBioinformaticsStatisticsCellular biologyBayesian statisticscell cyclecell growthimage analysissystems biology<p>Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, <italic>Saccharomyces cerevisiae</italic>, these two processes are known to be coordinated with one another as a cell's mass must roughly double before division. Moreover, cell-cycle progression is dependent on cell size with smaller cells at birth generally taking more time in the cell cycle. This dependence is a signature of size control. Systems biology is an emerging field that emphasizes connections or dependencies between biological entities and processes over the characteristics of individual entities. Statistical models provide a quantitative framework for describing and analyzing these dependencies. In this dissertation, I take a statistical systems biology approach to study cell division and growth and the dependencies within and between these two processes, drawing on observations from richly informative microscope images and time-lapse movies. I review the current state of knowledge on these processes, highlighting key results and open questions from the biological literature. I then discuss my development of machine learning and statistical approaches to extract cell-cycle information from microscope images and to better characterize the cell-cycle progression of populations of cells. In addition, I analyze single cells to uncover correlation in cell-cycle progression, evaluate potential models of dependence between growth and division, and revisit classical assertions about budding yeast size control. This dissertation presents a unique perspective and approach towards comprehensive characterization of the coordination between growth and division.</p>DissertationHartemink, Alexander JHaase, Steven B2014Dissertationhttp://hdl.handle.net/10161/9089
collection NDLTD
sources NDLTD
topic Bioinformatics
Statistics
Cellular biology
Bayesian statistics
cell cycle
cell growth
image analysis
systems biology
spellingShingle Bioinformatics
Statistics
Cellular biology
Bayesian statistics
cell cycle
cell growth
image analysis
systems biology
Mayhew, Michael Benjamin
Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
description <p>Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, <italic>Saccharomyces cerevisiae</italic>, these two processes are known to be coordinated with one another as a cell's mass must roughly double before division. Moreover, cell-cycle progression is dependent on cell size with smaller cells at birth generally taking more time in the cell cycle. This dependence is a signature of size control. Systems biology is an emerging field that emphasizes connections or dependencies between biological entities and processes over the characteristics of individual entities. Statistical models provide a quantitative framework for describing and analyzing these dependencies. In this dissertation, I take a statistical systems biology approach to study cell division and growth and the dependencies within and between these two processes, drawing on observations from richly informative microscope images and time-lapse movies. I review the current state of knowledge on these processes, highlighting key results and open questions from the biological literature. I then discuss my development of machine learning and statistical approaches to extract cell-cycle information from microscope images and to better characterize the cell-cycle progression of populations of cells. In addition, I analyze single cells to uncover correlation in cell-cycle progression, evaluate potential models of dependence between growth and division, and revisit classical assertions about budding yeast size control. This dissertation presents a unique perspective and approach towards comprehensive characterization of the coordination between growth and division.</p> === Dissertation
author2 Hartemink, Alexander J
author_facet Hartemink, Alexander J
Mayhew, Michael Benjamin
author Mayhew, Michael Benjamin
author_sort Mayhew, Michael Benjamin
title Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
title_short Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
title_full Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
title_fullStr Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
title_full_unstemmed Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
title_sort computational systems biology of saccharomyces cerevisiae cell growth and division
publishDate 2014
url http://hdl.handle.net/10161/9089
work_keys_str_mv AT mayhewmichaelbenjamin computationalsystemsbiologyofsaccharomycescerevisiaecellgrowthanddivision
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