Desynchronization rate in cell populations: mathematical modeling and experimental data
- PMID: 11162063
- DOI: 10.1006/jtbi.2000.2213
Desynchronization rate in cell populations: mathematical modeling and experimental data
Abstract
We characterize the kinetics of two cancer cell lines: IGROV1 (ovarian carcinoma) and MOLT4 (leukemia). By means of flow cytometry, we selected two populations from exponentially growing in vitro cell lines, depending on the cells' DNA synthesis activity during a preceding labeling period. For these populations we determined the time course of the percentages of cells in different phases of the cycles, sampling every 3 hr for 60 hr. Initially, semi-synchronous populations quickly converged to a stable age distribution, which is typical of the cell line (at equilibrium); this desynchronization reflects the intercell variability in cell cycle duration. By matching these experimental observations to mathematical modelling, we related the convergence rate toward the asymptotic distribution (R) and the period of the phase-percentage oscillations (T), to the mean cell cycle duration and its coefficient of variation. We give two formulas involving the above-mentioned parameters. Since T and R can be drawn by fitting our data to an asymptotic formula obtained from the model, we can estimate the other two kinetic parameters. IGROV1 cells have a shorter mean cell cycle time, but higher intercell variability than the leukemia line, which takes longer to lose synchrony.
Copyright 2001 Academic Press.
Similar articles
-
Kinetic heterogeneity of an experimental tumour revealed by BrdUrd incorporation and mathematical modelling.Bull Math Biol. 2002 Mar;64(2):355-84. doi: 10.1006/bulm.2001.0280. Bull Math Biol. 2002. PMID: 11926121
-
Modelling the flow [corrected] cytometric data obtained from unperturbed human tumour cell lines: parameter fitting and comparison.Bull Math Biol. 2005 Jul;67(4):815-30. doi: 10.1016/j.bulm.2004.10.003. Epub 2004 Dec 15. Bull Math Biol. 2005. PMID: 15893554
-
[Mathematical methods in flow cytometry: the problem of evaluating DNA histograms of partially synchronous cell populations].Acta Biol Med Ger. 1982;41(9):787-99. Acta Biol Med Ger. 1982. PMID: 7164698 German.
-
[Establishment of human ovarian carcinoma cell lines with directional highly lymphatic metastasis and study of their biological characteristics].Zhonghua Fu Chan Ke Za Zhi. 2007 Jul;42(7):482-6. Zhonghua Fu Chan Ke Za Zhi. 2007. PMID: 17961340 Chinese.
-
Analysis and modeling of growing budding yeast populations at the single cell level.Cytometry A. 2009 Feb;75(2):114-20. doi: 10.1002/cyto.a.20689. Cytometry A. 2009. PMID: 19085920 Review.
Cited by
-
A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.Ann Appl Stat. 2009 Winter;3(4):1521-1541. doi: 10.1214/09-AOAS264. Ann Appl Stat. 2009. PMID: 21853014 Free PMC article.
-
A checkpoint-oriented cell cycle simulation model.Cell Cycle. 2019 Apr;18(8):795-808. doi: 10.1080/15384101.2019.1591125. Epub 2019 Apr 4. Cell Cycle. 2019. PMID: 30870080 Free PMC article.
-
Are Tumor Cell Lineages Solely Shaped by Mechanical Forces?Bull Math Biol. 2017 Oct;79(10):2356-2393. doi: 10.1007/s11538-017-0333-y. Epub 2017 Aug 29. Bull Math Biol. 2017. PMID: 28852950 Free PMC article.
-
The application of mathematical modelling to aspects of adjuvant chemotherapy scheduling.J Math Biol. 2004 Apr;48(4):375-422. doi: 10.1007/s00285-003-0246-2. Epub 2003 Oct 27. J Math Biol. 2004. PMID: 15052504
-
Accurate delineation of cell cycle phase transitions in living cells with PIP-FUCCI.Cell Cycle. 2018;17(21-22):2496-2516. doi: 10.1080/15384101.2018.1547001. Cell Cycle. 2018. PMID: 30421640 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources