A general approach to the statistics of microbial orientation: L\'{e}vy walks, noise, and deterministic motion

TL;DR

This paper introduces a unified statistical framework for microbial orientation, modeling their angular dynamics with a Voigt profile that combines Gaussian and Lorentzian noise, and accounts for deterministic swimming behaviors.

Contribution

It presents a novel continuous Le9vy flight model for microbial tumbling, supported by an ensemble theory that incorporates deterministic swimming patterns.

Findings

Angular statistics follow a Voigt profile across microbes.

Lorentzian noise significantly influences microbial orientation.

The model estimates physical parameters like rotational diffusion and noise strength.

Abstract

Microbial motion is typically analyzed by simplified models in which trajectories exhibit straight runs (perhaps with added Gaussian noise) followed by random, discrete tumbling events. We present the results of a statistical analysis of the angular dynamics for four different swimming microbes: tumbling and smooth-swimming strains of \textit{Bacillus subtilis} and two Eukaryotic algae, \textit{Tetraselmis suecica} and \textit{Euglena gracilis}. We show that the angular statistics closely resemble a Voigt profile, the convolution of a Gaussian (L\'{e}vy index $\alpha=2$) and Lorentzian (L\'{e}vy index $\alpha=1$) distribution. This distribution is ubiquitous for all four microbes. Rather than modeling tumbling as a discrete process, we model tumbling dynamics as a continuous process: L\'{e}vy flights in the orientational dynamics using a Lorentzian noise model. This model is analytically solvable. Each individual microbe trajectory has both stochastic behavior (noise) and varying deterministic behavior, such as helices of different sizes and frequencies and circular arcs with different radii. We model the distribution of different deterministic behavior via an ensemble theory. The deterministic behavior (e.g., circular arcs) comes from physical observations of the swimming behavior and explains many of the qualitative features in the data that cannot be explained by a pure noise model. From this theory, we estimate the strength of Lorentzian noise, the physical rotational diffusion constant, and some relevant parameters relating to the distributions of deterministic behavior. This analysis shows that in some cases Gaussian noise is not the dominant process responsible for the angular statistics following a Voigt profile.