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Related Experiment Video

Updated: Jul 16, 2026

3D Organotypic Co-culture Model Supporting Medullary Thymic Epithelial Cell Proliferation, Differentiation and Promiscuous Gene Expression

3D Organotypic Co-culture Model Supporting Medullary Thymic Epithelial Cell Proliferation, Differentiation and Promiscuous Gene Expression

Published on: July 30, 2015

Modeling T- and B-cell growth and differentiation.

Robin Callard¹, Phil Hodgkin

¹Immunobiology Unit, Institute of Child Health, University College London, London, UK. rcallard@ich.ucl.ac.uk

Immunological Reviews

|March 21, 2007

Summary

Mathematical models and CFSE experiments help understand T- and B-cell proliferation control. This approach reveals how cell division, death, and cycle numbers integrate to shape adaptive immune responses.

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Mouse Naïve CD4+ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Mouse Naïve CD4⁺ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Published on: April 16, 2015

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Last Updated: Jul 16, 2026

3D Organotypic Co-culture Model Supporting Medullary Thymic Epithelial Cell Proliferation, Differentiation and Promiscuous Gene Expression

3D Organotypic Co-culture Model Supporting Medullary Thymic Epithelial Cell Proliferation, Differentiation and Promiscuous Gene Expression

Published on: July 30, 2015

Mouse Naïve CD4+ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Mouse Naïve CD4⁺ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Published on: April 16, 2015

Area of Science:

Immunology
Computational Biology
Systems Biology

Background:

Adaptive immune responses rely on T- and B-cell proliferation after antigen stimulation.
The dynamics of cell division, death, and cycle number determine the response outcome.
Understanding the integration of signals controlling these processes is crucial for immune response regulation.

Purpose of the Study:

To review how mathematical models and CFSE data can investigate T- and B-cell proliferation control mechanisms.
To explore the integration of signals governing cell division, death, and division cycles.

Main Methods:

Utilizing mathematical modeling to simulate cellular dynamics.
Analyzing data from carboxyfluorescein diacetate succinamidyl ester (CFSE) experiments.
Integrating computational and experimental approaches to study immune cell proliferation.

Main Results:

Demonstrates the utility of combining mathematical models with CFSE data for dissecting proliferation control.
Highlights how different control mechanisms influence T- and B-cell responses.
Provides insights into the complex interplay of factors regulating adaptive immunity.

Conclusions:

Mathematical modeling and CFSE experiments are powerful tools for understanding adaptive immunity.
The integration of various regulatory signals dictates the precise outcome of T- and B-cell responses.
Further research using these combined methods can advance our knowledge of immune cell dynamics.