Sources, propagation and consequences of stochasticity in cellular growth

Philipp Thomas,Guillaume Terradot,Vincent Danos,Andrea Y Weiße

doi:10.1038/s41467-018-06912-9

Abstract

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.

Highlights

Growth impacts a range of phenotypic responses
We present a stochastic model of single-cell bacterial dynamics to predict the growth rate of individual cells
We identify dynamics of mRNAs coding for nutrient transporters and enzymes as a major source of growth rate fluctuations

Summary

Introduction

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can unravel mechanisms that underpin cell decisions. For example, is inherently stochastic at the single-cell level[12] It is less clear though how such variation affects other intracellular processes[14,15], and how it translates to phenotypic differences and cell fitness. Our description of cells is based on biochemical kinetics, which accounts for stochastic fluctuations in cellular mechanisms giving rise to heterogeneous responses. In this context, the magnitude of fluctuations results from the abundance of key molecular players[22], and so we can predict emergent growth variations rather than impose them onto the model behaviour

Methods

Results

Conclusion