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Numerical modelling of label-structured cell population growth using CFSE distribution data
BACKGROUND: The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distr...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950697/ https://www.ncbi.nlm.nih.gov/pubmed/17650320 http://dx.doi.org/10.1186/1742-4682-4-26 |
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author | Luzyanina, Tatyana Roose, Dirk Schenkel, Tim Sester, Martina Ehl, Stephan Meyerhans, Andreas Bocharov, Gennady |
author_facet | Luzyanina, Tatyana Roose, Dirk Schenkel, Tim Sester, Martina Ehl, Stephan Meyerhans, Andreas Bocharov, Gennady |
author_sort | Luzyanina, Tatyana |
collection | PubMed |
description | BACKGROUND: The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distributed parameter mathematical models and computational techniques for data assimilation. METHODS AND RESULTS: The mathematical modelling of label-structured cell population dynamics leads to a hyperbolic partial differential equation in one space variable. The model contains fundamental parameters of cell turnover and label dilution that need to be estimated from the flow cytometry data on the kinetics of the CFSE label distribution. To this end a maximum likelihood approach is used. The Lax-Wendroff method is used to solve the corresponding initial-boundary value problem for the model equation. By fitting two original experimental data sets with the model we show its biological consistency and potential for quantitative characterization of the cell division and death rates, treated as continuous functions of the CFSE expression level. CONCLUSION: Once the initial distribution of the proliferating cell population with respect to the CFSE intensity is given, the distributed parameter modelling allows one to work directly with the histograms of the CFSE fluorescence without the need to specify the marker ranges. The label-structured model and the elaborated computational approach establish a quantitative basis for more informative interpretation of the flow cytometry CFSE systems. |
format | Text |
id | pubmed-1950697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-19506972007-08-23 Numerical modelling of label-structured cell population growth using CFSE distribution data Luzyanina, Tatyana Roose, Dirk Schenkel, Tim Sester, Martina Ehl, Stephan Meyerhans, Andreas Bocharov, Gennady Theor Biol Med Model Research BACKGROUND: The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distributed parameter mathematical models and computational techniques for data assimilation. METHODS AND RESULTS: The mathematical modelling of label-structured cell population dynamics leads to a hyperbolic partial differential equation in one space variable. The model contains fundamental parameters of cell turnover and label dilution that need to be estimated from the flow cytometry data on the kinetics of the CFSE label distribution. To this end a maximum likelihood approach is used. The Lax-Wendroff method is used to solve the corresponding initial-boundary value problem for the model equation. By fitting two original experimental data sets with the model we show its biological consistency and potential for quantitative characterization of the cell division and death rates, treated as continuous functions of the CFSE expression level. CONCLUSION: Once the initial distribution of the proliferating cell population with respect to the CFSE intensity is given, the distributed parameter modelling allows one to work directly with the histograms of the CFSE fluorescence without the need to specify the marker ranges. The label-structured model and the elaborated computational approach establish a quantitative basis for more informative interpretation of the flow cytometry CFSE systems. BioMed Central 2007-07-24 /pmc/articles/PMC1950697/ /pubmed/17650320 http://dx.doi.org/10.1186/1742-4682-4-26 Text en Copyright © 2007 Luzyanina et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Luzyanina, Tatyana Roose, Dirk Schenkel, Tim Sester, Martina Ehl, Stephan Meyerhans, Andreas Bocharov, Gennady Numerical modelling of label-structured cell population growth using CFSE distribution data |
title | Numerical modelling of label-structured cell population growth using CFSE distribution data |
title_full | Numerical modelling of label-structured cell population growth using CFSE distribution data |
title_fullStr | Numerical modelling of label-structured cell population growth using CFSE distribution data |
title_full_unstemmed | Numerical modelling of label-structured cell population growth using CFSE distribution data |
title_short | Numerical modelling of label-structured cell population growth using CFSE distribution data |
title_sort | numerical modelling of label-structured cell population growth using cfse distribution data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950697/ https://www.ncbi.nlm.nih.gov/pubmed/17650320 http://dx.doi.org/10.1186/1742-4682-4-26 |
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