Dynamical Inference of Cell Size Regulation Parameters.

Area of Science:

• Cell Biology
• Mathematical Biology
• Systems Biology

Background:

• Cells maintain size homeostasis through division timing regulated by size, added size, and cell cycle duration.
• Previous studies confirmed mechanism robustness under steady-state conditions.
• Dynamic responses in fluctuating environments remain poorly understood.

Purpose of the Study:

• To introduce a novel computational model for cell size dynamics in fluctuating environments.
• To characterize cell division processes using a three-parameter framework.
• To develop a robust statistical framework for analyzing experimental data.

Main Methods:

• Utilized a piecewise deterministic Markov chain framework to model cell division as stochastic jumps.
• Defined division propensity based on current cell size and added size.
• Developed a maximum likelihood estimation (MLE) framework using derived analytical formulas for division probability.

Main Results:

• Proposed a three-parameter characterization: scale, shape, and division strategy.
• Derived analytical formulas for division probability.
• Demonstrated model accuracy and performance across various dynamic scenarios through systematic investigation.

Conclusions:

• The proposed model offers a powerful tool for analyzing cell size regulation in dynamic environments.
• The framework accurately infers model parameters from experimental data.
• This approach enhances the understanding of cell homeostasis under changing conditions.