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Related Experiment Video

Updated: Jun 15, 2026

The Use of Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE) to Monitor Lymphocyte Proliferation
04:10

The Use of Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE) to Monitor Lymphocyte Proliferation

Published on: October 12, 2010

Estimation of cell proliferation dynamics using CFSE data.

H T Banks1, Karyn L Sutton, W Clayton Thompson

  • 1Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212, USA. htbanks@ncsu.edu

Bulletin of Mathematical Biology
|March 3, 2010
PubMed
Summary
This summary is machine-generated.

This study enhances a mathematical model for cell proliferation using fluorescence intensity. New decay and time-dependent rates improve model accuracy for cell dynamics and biomedical applications.

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Published on: June 4, 2019

Area of Science:

  • Mathematical Biology
  • Cell Biology
  • Biomedical Modeling

Background:

  • Quantitative studies of cell proliferation rely on fluorescent labeling techniques like flow cytometry.
  • Carboxyfluorescein succinimidyl ester (CFSE) labeling allows tracking of proliferating cells over time.
  • Existing mathematical models describe cell evolution based on fluorescence intensity.

Purpose of the Study:

  • To discuss and extend a mathematical model for proliferating cells labeled with CFSE.
  • To improve model-data agreement through modifications and statistical analysis.
  • To enhance uncertainty quantification in cell dynamics modeling.

Main Methods:

  • Utilizing fluorescence intensity as a structural variable in a mathematical model.
  • Developing and analyzing extensions and modifications to the existing model.
  • Applying statistical models to account for data variability and noise.

Main Results:

  • New models incorporating label decay/loss and time-dependent proliferation/death rates show improved fits to experimental data.
  • Statistical analysis confirms the significance of proposed model improvements.
  • Enhanced understanding of cell dynamics through refined mathematical modeling.

Conclusions:

  • The refined cell dynamics model offers better accuracy for proliferation assay tracking and modeling.
  • The model has broad applications in various biomedical sciences.
  • Improved statistical methods enhance the reliability of proliferation studies.