A transition probability cell cycle model simulation of bivariate DNA/bromodeoxyuridine distributions

Area of Science:

• Cell Biology
• Biophysics
• Mathematical Modeling

Background:

• Cell cycle analysis is crucial for understanding cell proliferation and development.
• Existing models face challenges in accurately distinguishing cell cycle variability from experimental noise.
• Bromodeoxyuridine (BrdUrd) incorporation is a key marker for DNA synthesis during the S-phase of the cell cycle.

Purpose of the Study:

• To extend the transition probability cell cycle model to incorporate cell cycle variability and BrdUrd incorporation.
• To simulate BrdUrd uptake in both pulse-chase and continuous-labeling experiments.
• To differentiate variability in cell cycle progression from errors in data acquisition.

Main Methods:

• Developed an extended transition probability cell cycle model.
• Incorporated random transitions to distinguish cell cycle progression variability.
• Simulated bivariate DNA/BrdUrd distributions for pulse-chase and continuous-labeling experiments.
• Compared the model's performance against a previously established compartmental model.

Main Results:

• The enhanced model realistically simulates BrdUrd uptake and cell cycle dynamics.
• It requires fewer parameters compared to existing models.
• The model effectively describes gradual asynchronization of cell cycle cohorts.
• Simulated experiments revealed potential overestimation of unlabeled cell fractions in bivariate distribution analysis.

Conclusions:

• The extended transition probability model provides a robust framework for analyzing cell cycle variability and BrdUrd incorporation.
• Accurate parameter estimation relies on matching cell cycle cohort movements rather than solely on unlabeled cell fractions.
• The model offers a more parsimonious and accurate approach to cell cycle modeling.