Characterizing stochastic cell cycle dynamics in exponential growth

TL;DR

This paper links deterministic and stochastic models of cell cycle dynamics, showing that exponential culture demographics can be accurately described by deterministic models using exponential mean lifetimes, with broad applicability in cell biology.

Contribution

It introduces a stochastic extension to existing deterministic cell cycle models and establishes an exact relationship between their parameters, enhancing understanding of cell cycle variability.

Findings

Deterministic and stochastic models are connected via exponential mean lifetimes.

Exponential culture demographics are well-fit by deterministic models despite stochastic timing.

Models have broad applicability beyond E. coli in cell cycle analysis.

Abstract

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.