Abstract

Unraveling bacterial strategies for spatial exploration is crucial for understanding the complexity in the organization of life. Bacterial motility determines the spatio-temporal structure of microbial communities, controls infection spreading and the microbiota organization in guts or in soils. Most theoretical approaches for modeling bacterial transport rely on their run-and-tumble motion. For Escherichia coli, the run time distribution was reported to follow a Poisson process with a single characteristic time related to the rotational switching of the flagellar motors. However, direct measurements on flagellar motors show heavy-tailed distributions of rotation times stemming from the intrinsic noise in the chemotactic mechanism. Currently, there is no direct experimental evidence that the stochasticity in the chemotactic machinery affect the macroscopic motility of bacteria. In stark contrast with the accepted vision of run-and-tumble, here we report a large behavioral variability of wild-type \emph{E. coli}, revealed in their three-dimensional trajectories. At short observation times, a large distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal. Over long times, individual bacteria undergo significant changes in motility. We demonstrate that such a large distribution of run times introduces measurement biases in most practical situations. Our results reconcile the notorious conundrum between run time observations and motor switching statistics. We finally propose that statistical modeling of transport properties currently undertaken in the emerging framework of active matter studies, should be reconsidered under the scope of this large variability of motility features.

Highlights

  • The run-and-tumble (R&T) strategy developed by bacteria for exploring their environment is a cornerstone of quantitative modeling of bacterial transport

  • Individual bacteria undergo significant changes in motility. We demonstrate that such a large distribution of run times introduces measurement biases in most practical situations

  • They proposed that an adapted bacterium would perform, over long times, an isotropic random walk composed of the run-and-tumble phases, both distributed in time as a Poisson process [1–5]

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Summary

INTRODUCTION

The run-and-tumble (R&T) strategy developed by bacteria for exploring their environment is a cornerstone of quantitative modeling of bacterial transport. Korobkova et al [21] brought evidence for a heavy tail distribution for the duration of CCW rotations This highlights possible coupling between the stochastic fluctuations in the chemotactic biochemical network and the emergent bacterial motility. The model is here adapted to render the spatial exploration process It explains the occurrence of a large behavioral variability of swimming direction and why, at short observation times, a large distribution of these is expected over a population. We discuss the consequences of measuring averaged quantities over a population displaying a large distribution of motility features This source of measurement bias is relevant in the general framework of experiments on statistical physics of active matter

VARIABILITY OF BACTERIAL MOTILITY IN A POPULATION
MOTILITY AND MOTOR ROTATION STATISTICS
Quantitative description of the behavioral variability model
Memory time
Comparison with the model
Experiments I
CONCLUSIONS
Bacterial strains and culture
The 3D Lagrangian tracker
Experimental geometries and bacteria tracking
Track simulations using the BV model
Full Text
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