Dynamical Inference of Cell Size Regulation Parameters

TL;DR

This paper introduces a novel stochastic modeling framework for analyzing cell size regulation, enabling inference of key parameters from dynamic single-cell data, and demonstrates its effectiveness across various fluctuating environmental scenarios.

Contribution

It develops a piecewise deterministic Markov chain model with an MLE inference method to characterize cell division strategies and their dynamics in changing environments.

Findings

Model accurately infers division parameters from simulated data.

Framework captures dynamic changes in cell size regulation mechanisms.

Applicable to analyzing experimental data in fluctuating conditions.

Abstract

Cells achieve size homeostasis by regulating their division timing based on their size, added size, and cell cycle time. Previous research under steady-state conditions demonstrated the robustness of these mechanisms. However, their dynamic responses in fluctuating environments, such as nutrient depletion due to population growth, remain challenging to fully characterize. Currently, advances in single-cell microscopy have revealed various cellular division strategies whose underlying molecular mechanisms are complex and not always available. This study introduces a novel approach to model cell size dynamics using a piecewise deterministic Markov chain framework, where cell division events are modeled as stochastic jumps determined by a division propensity dependent on both current cell size and added size since birth. We propose a three-parameter characterization for the division process: scale (target added size at division), shape (division stochasticity), and division strategy (relevance of cell size, added size, or cell cycle duration). We derive analytical formulas for the probability of division, and with this probability, we develop a maximum likelihood estimation (MLE) framework. We implement a systematic investigation of the accuracy of inference as a function of sample size. The model's performance is studied across various scenarios, including those exhibiting dynamical changes in one or more parameters, suggesting its broad applicability for analyzing new experimental data on cell size regulation in dynamic environments.