Reconstruction of cell population dynamics using CFSE
<p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a ri...
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Series: | BMC Bioinformatics |
Online Access: | http://www.biomedcentral.com/1471-2105/8/196 |
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doaj-ffbc8f858f1e446eb84d5661d58dc06a2020-11-24T21:04:43ZengBMCBMC Bioinformatics1471-21052007-06-018119610.1186/1471-2105-8-196Reconstruction of cell population dynamics using CFSECallard RobinMoon SimonStrid JessicaChan CliburnYates AndrewGeorge Andrew JTStark Jaroslav<p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.</p> <p>Results</p> <p>We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.</p> <p>Conclusion</p> <p>The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.</p> http://www.biomedcentral.com/1471-2105/8/196 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Callard Robin Moon Simon Strid Jessica Chan Cliburn Yates Andrew George Andrew JT Stark Jaroslav |
spellingShingle |
Callard Robin Moon Simon Strid Jessica Chan Cliburn Yates Andrew George Andrew JT Stark Jaroslav Reconstruction of cell population dynamics using CFSE BMC Bioinformatics |
author_facet |
Callard Robin Moon Simon Strid Jessica Chan Cliburn Yates Andrew George Andrew JT Stark Jaroslav |
author_sort |
Callard Robin |
title |
Reconstruction of cell population dynamics using CFSE |
title_short |
Reconstruction of cell population dynamics using CFSE |
title_full |
Reconstruction of cell population dynamics using CFSE |
title_fullStr |
Reconstruction of cell population dynamics using CFSE |
title_full_unstemmed |
Reconstruction of cell population dynamics using CFSE |
title_sort |
reconstruction of cell population dynamics using cfse |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2007-06-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division <it>in vitro </it>and <it>in vivo </it>and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.</p> <p>Results</p> <p>We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.</p> <p>Conclusion</p> <p>The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.</p> |
url |
http://www.biomedcentral.com/1471-2105/8/196 |
work_keys_str_mv |
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