A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle...

Area of Science:

• Cell Biology
• Mathematical Modeling
• Systems Biology

Background:

• Intermitotic time (IMT), the duration of the cell cycle, exhibits significant variability even in genetically identical cells.
• Decades of research have proposed various mathematical models to explain this inherent cell cycle variability.
• Previous work established a stochastic drift-diffusion+threshold (DDT) model for cell cycle checkpoints, demonstrating its ability to fit experimental IMT distributions.

Purpose of the Study:

• To apply and extend the DDT modeling approach for descriptive and predictive analysis of cell cycle variability.
• To develop a robust numerical method for estimating model parameters without prior assumptions on the number of checkpoints.
• To identify the optimal number of checkpoints and their characteristics influencing IMT using diverse experimental data.

Main Methods:

• Development of a custom numerical method for maximum likelihood estimation of DDT model parameters.
• Fitting one-, two-, and three-checkpoint variants of the DDT model to IMT data from multiple cell lines under varying conditions (growth, drug treatments).
• Analysis of experimental data separating IMT into G1, S, G2, and M phases to correlate model predictions with specific cell cycle stages.

Main Results:

• A two-checkpoint DDT model provided the best fit to the experimental IMT data.
• The model identified distinct phases of the cell cycle: a highly variable, growth factor-sensitive phase and a less variable, growth factor-refractory phase.
• Experimental validation confirmed that the growth factor-sensitive phase corresponds to a portion of G1, aligning with the 'commitment to divide' concept.

Conclusions:

• The study validates a parsimonious two-checkpoint stochastic model for explaining cell cycle time variability.
• The G1 phase commitment step is identified as the primary driver of intermitotic time variability and growth factor sensitivity.
• This modeling approach offers fundamental insights into the biological mechanisms governing cell cycle progression and its variability.