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

TL;DR

This paper introduces a flexible branching process model for analyzing cell population dynamics in synchrony experiments, enabling detailed deconvolution of measurements and comparison across experiments.

Contribution

It presents a novel, constructively formulated branching process model that can incorporate various data types and attribute effects to specific cell cohorts, enhancing analysis of cell cycle experiments.

Findings

Model accurately fits budding index and DNA content data

Enables in silico synchronization of cell populations

Facilitates comparison of measurements across experiments

Abstract

We present a flexible branching process model for cell population dynamics in synchrony/time-series experiments used to study important cellular processes. Its formulation is constructive, based on an accounting of the unique cohorts in the population as they arise and evolve over time, allowing it to be written in closed form. The model can attribute effects to subsets of the population, providing flexibility not available using the models historically applied to these populations. It provides a tool for in silico synchronization of the population and can be used to deconvolve population-level experimental measurements, such as temporal expression profiles. It also allows for the direct comparison of assay measurements made from multiple experiments. The model can be fit either to budding index or DNA content measurements, or both, and is easily adaptable to new forms of data. The ability to use DNA content data makes the model applicable to almost any organism. We describe the model and illustrate its utility and flexibility in a study of cell cycle progression in the yeast Saccharomyces cerevisiae.