Quantitative characterization of run-and-tumble statistics in bulk bacterial suspensions

TL;DR

This paper presents a new numerical method to accurately extract run-and-tumble parameters from experimental data on bacterial suspensions, improving understanding of bacterial motility.

Contribution

The authors develop a real-time analysis approach using renewal processes, validated with simulations and applied to experimental E. coli data, advancing motility measurement techniques.

Findings

Validated method against agent-based simulations

Identified key length and time scales for measurements

Applied approach successfully to experimental data

Abstract

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy.