Mathematical Modeling of p27-Regulated Quiescent-to-Proliferative Transition: Parameter Uncertainty Quantification,...

Area of Science:

• Cell Biology
• Mathematical Biology
• Cancer Research

Background:

• Cellular quiescence to proliferation is regulated by CyclinD/E kinase complexes.
• The p27 protein's role in activating these complexes during the cell cycle switch is not fully understood.

Purpose of the Study:

• To develop a quantitative model of p27-mediated activation of CyclinD- and CyclinE-associated kinase complexes.
• To characterize the regulatory mechanisms governing the quiescent-to-proliferative cell transition.

Main Methods:

• An ordinary differential equation (ODE) model was established to simulate kinase complex activation.
• Model parameters were estimated using the quantile-based Physics-Informed Neural Network (PINN) method with existing experimental data.
• Uncertainty quantification was performed for estimated parameters and model predictions.

Main Results:

• Parameter estimation revealed distinct modal values and variations in kernel density estimation (KDE) curves.
• The interplay between model structure and data quality influenced parameter uncertainty.
• Variable- and time-dependent predictive uncertainty was successfully propagated, establishing reliable prediction ranges.

Conclusions:

• The study provides a quantitative understanding of p27's role in cell cycle control.
• The developed model offers an interpretable framework for investigating cancer-targeted intervention strategies.
• This work facilitates the rational design of therapies for cell cycle dysregulation in cancer.