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Cell size distribution of lineage data: analytic results and parameter inference
Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. These data encode information on how cells correct size aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961097/ https://www.ncbi.nlm.nih.gov/pubmed/33748708 http://dx.doi.org/10.1016/j.isci.2021.102220 |
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author | Jia, Chen Singh, Abhyudai Grima, Ramon |
author_facet | Jia, Chen Singh, Abhyudai Grima, Ramon |
author_sort | Jia, Chen |
collection | PubMed |
description | Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. These data encode information on how cells correct size aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions. |
format | Online Article Text |
id | pubmed-7961097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-79610972021-03-19 Cell size distribution of lineage data: analytic results and parameter inference Jia, Chen Singh, Abhyudai Grima, Ramon iScience Article Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. These data encode information on how cells correct size aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions. Elsevier 2021-02-24 /pmc/articles/PMC7961097/ /pubmed/33748708 http://dx.doi.org/10.1016/j.isci.2021.102220 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Jia, Chen Singh, Abhyudai Grima, Ramon Cell size distribution of lineage data: analytic results and parameter inference |
title | Cell size distribution of lineage data: analytic results and parameter inference |
title_full | Cell size distribution of lineage data: analytic results and parameter inference |
title_fullStr | Cell size distribution of lineage data: analytic results and parameter inference |
title_full_unstemmed | Cell size distribution of lineage data: analytic results and parameter inference |
title_short | Cell size distribution of lineage data: analytic results and parameter inference |
title_sort | cell size distribution of lineage data: analytic results and parameter inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961097/ https://www.ncbi.nlm.nih.gov/pubmed/33748708 http://dx.doi.org/10.1016/j.isci.2021.102220 |
work_keys_str_mv | AT jiachen cellsizedistributionoflineagedataanalyticresultsandparameterinference AT singhabhyudai cellsizedistributionoflineagedataanalyticresultsandparameterinference AT grimaramon cellsizedistributionoflineagedataanalyticresultsandparameterinference |