Identification of age-structured models: cell cycle phase transitions.

Area of Science:

• Cell Biology
• Biophysics
• Mathematical Modeling

Background:

• Age-structured models offer detailed insights into cellular behavior but are challenging to fit to experimental data.
• Existing methods struggle with direct observation of age distributions in cell populations.
• Understanding cell cycle dynamics is crucial for cancer research and drug development.

Purpose of the Study:

• To develop and validate a methodology for determining age-specific cell cycle transition rates during balanced growth.
• To apply this methodology to human leukemia (Jurkat) cells using limited experimental data.
• To identify age distributions and transition-age probability distributions.

Main Methods:

• Utilized age-structured population balance equations for modeling.
• Employed bromodeoxyuridine labeling to create distinct cell subpopulations.
• Fitted experimental total cell number density data using cubic spline nodes for transition rate representation.

Main Results:

• Successfully identified age-specific transition rates and age distributions in Jurkat cells.
• A bimodal G(0)/G(1) transition age probability distribution provided the best fit to experimental data.
• Alternative distributions (Gaussian, lognormal) corroborated spline predictions with fewer parameters, though a single lognormal fit was inferior.

Conclusions:

• The developed methodology effectively determines age distributions and age-specific transition rates under balanced growth conditions.
• The findings suggest a potentially bimodal nature of cell cycle transitions in Jurkat cells.
• This approach provides a robust framework for analyzing cell cycle dynamics in various cell types.