Literature DB >> 1462731

Flow cytometric analysis of the cell cycle: mathematical modeling and biological interpretation.

J Pierrez1, X Ronot.   

Abstract

Estimation of the repartition of asynchronous cells in the cell cycle can be explained by two hypotheses: the cells are supposed to be distributed into three groups: cells with a 2c DNA content (G0/1 phase), cells with a 4c DNA content (G2 + M phase) and cells with a DNA content ranging from 2c to 4c (S phase); there is a linear relationship between the amount of fluorescence emitted by the fluorescent probe which reveals the DNA and the DNA content. According to these hypotheses, the cell cycle can be represented by the following equation: [formula: see text] All the solutions for this equation are approximations. Non parametric methods (or graphical methods: rectangle, peak reflect) only use one or two phase(s) of the cell cycle, the remaining phase(s) being estimated by exclusion. In parametric methods (Dean & Jett, Baisch II, Fried), the DNAT(x) distribution is supposed to be known and is composed of two gaussians (representative of G0/1 and G2 + M) and a P(x,y) function representative of S phase. Despite the generality, these models are not applicable to all sample types, particularly heterogeneous cell populations with various DNA content. In addition, the cell cycle is dependent on several regulation points (transition from quiescence to proliferation, DNA synthesis initiation, mitosis induction) and biological perturbations can also lead to cytokinesis perturbations. Before the emergence of flow cytometry, the current view of cell cycle resided in the assessment of cell proliferation (increase in cell number) or the kinetic of molecules incorporation (DNA precursors).(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1992        PMID: 1462731     DOI: 10.1007/bf00168142

Source DB:  PubMed          Journal:  Acta Biotheor        ISSN: 0001-5342            Impact factor:   1.774


  10 in total

1.  Analysis of PCP-data to determine the fraction of cells in the various phases of cell cycle.

Authors:  H Baisch; W Göhde; W A Linden
Journal:  Radiat Environ Biophys       Date:  1975-06-13       Impact factor: 1.925

Review 2.  The continuum model: an experimental and theoretical challenge to the G1 model of cell cycle regulation.

Authors:  A Okuda; S Cooper
Journal:  Exp Cell Res       Date:  1989-11       Impact factor: 3.905

3.  Pulse cytophotometric analysis of synchronized cells in vitro.

Authors:  B Barlogie; B Drewinko; D A Johnston; T Büchner; W H Hauss; E J Freireich
Journal:  Cancer Res       Date:  1976-03       Impact factor: 12.701

4.  Method for the quantitative evaluation of data from flow microfluorometry.

Authors:  J Fried
Journal:  Comput Biomed Res       Date:  1976-06

5.  Differential effect of D-penicillamine on the cell kinetic parameters of various normal and transformed cellular types.

Authors:  L Friteau; P Jaffray; X Ronot; M Adolphe
Journal:  J Cell Physiol       Date:  1988-09       Impact factor: 6.384

6.  A model for the computer analysis of synchronous DNA distributions obtained by flow cytometry.

Authors:  M H Fox
Journal:  Cytometry       Date:  1980-07

7.  Is the central dogma of flow cytometry true: that fluorescence intensity is proportional to cellular dye content?

Authors:  M Kerker; M A Van Dilla; A Brunsting; J P Kratohvil; P Hsu; D S Wang; J W Gray; R G Langlois
Journal:  Cytometry       Date:  1982-09

8.  A mathematical model of the mitotic cycle and its application to the interpretation of percentage labeled mitoses data.

Authors:  J C Barrett
Journal:  J Natl Cancer Inst       Date:  1966-10       Impact factor: 13.506

9.  Effect of adriamycin on the cell cycle traverse and kinetics of cultured human lymphoblasts.

Authors:  A Krishan; E Frei
Journal:  Cancer Res       Date:  1976-01       Impact factor: 12.701

10.  G2 arrest, binucleation, and single-parameter DNA flow cytometric analysis.

Authors:  X Ronot; C Hecquet; S Larno; B Hainque; M Adolphe
Journal:  Cytometry       Date:  1986-05
  10 in total
  2 in total

1.  A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.

Authors:  David A Orlando; Edwin S Iversen; Alexander J Hartemink; Steven B Haase
Journal:  Ann Appl Stat       Date:  2009       Impact factor: 2.083

2.  Silk-Derived Protein Enhances Corneal Epithelial Migration, Adhesion, and Proliferation.

Authors:  Waleed Abdel-Naby; Brigette Cole; Aihong Liu; Jingbo Liu; Pengxia Wan; Victor H Guaiquil; Ryan Schreiner; David Infanger; Brian D Lawrence; Mark I Rosenblatt
Journal:  Invest Ophthalmol Vis Sci       Date:  2017-03-01       Impact factor: 4.799

  2 in total

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