A branching process model for flow cytometry and budding index measurements in cell synchrony experiments

Area of Science:

• Cell Biology
• Mathematical Modeling
• Systems Biology

Background:

• Cell population dynamics are crucial for understanding cellular processes.
• Existing models often lack flexibility for time-series and synchrony experiments.
• Analyzing temporal expression profiles and deconvloving population-level measurements presents challenges.

Purpose of the Study:

• To introduce a flexible branching process model for cell population dynamics.
• To provide a tool for in silico synchronization and deconvolution of experimental data.
• To enable direct comparison of assay measurements across multiple experiments.

Main Methods:

• A constructive, closed-form branching process model based on cohort accounting.
• Model fitting to budding index and/or DNA content measurements.
• Application to cell cycle progression analysis in Saccharomyces cerevisiae.

Main Results:

• The model offers flexibility in attributing effects to population subsets.
• Enables in silico synchronization and deconvolution of temporal expression profiles.
• Facilitates direct comparison of data from multiple experiments.

Conclusions:

• The presented branching process model offers a flexible and adaptable tool for analyzing cell population dynamics.
• It enhances the study of cellular processes by enabling sophisticated data analysis and comparison.
• The model's adaptability, including the use of DNA content data, broadens its applicability across organisms.