Statistical signatures of microorganism motility under environmental stress
Abstract
The motility of microorganisms provides a natural framework for studying active matter and its response to environmental perturbations. We experimentally investigate the trajectories of \textit{Paramecium caudatum} and \textit{Artemia salina} under controlled conditions. Using video microscopy, we reconstruct individual trajectories over time. Beyond baseline conditions, we analyze stressed environments where \textit{P. caudatum} are exposed to a toxic agent and \textit{A. salina} are placed in distilled water. To characterize the motion, we compute trajectory-based metrics, including mean square displacement, velocity distributions, persistence times, tortuosity, and radius of gyration. These observables quantify changes in motility patterns across species and conditions. To interpret the dynamics, we develop coarse-grained stochastic models parameterized directly from experimental data. \textit{P. caudatum} is described within a run-and-tumble framework, including reversal events under stress. In contrast, \textit{A. salina} requires a run-and-tumble-like description with persistent orientational noise, accounting for continuous directional fluctuations during runs. The models reproduce the main trajectory-level observables used for comparison, namely tortuosity and radius of gyration. This combined approach provides insight into how environmental stress modifies locomotion statistics. Our results highlight systematic differences in the statistical signatures of motion, suggesting that trajectory metrics may serve as indicators of physiological stress. This work provides an accessible framework for active matter experiments where individual-level statistics can be directly measured and modeled.
Versions
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BibTeX
@misc{mossi2026statistical,
title = {{Statistical signatures of microorganism motility under environmental stress}},
author = {Luiza Treichel Mossi and Nicole Mazzitelli Narvaz and Larissa da Silva Bento and Roger Modena and Bernardo Boatini and Amalia Garcez and Leonardo Gregory Brunnet and Alexandre Arenzon and Carolina Brito},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25403},
eprint = {2609.25403},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25403},
}
RIS
TY - UNPB
TI - Statistical signatures of microorganism motility under environmental stress
AU - Luiza Treichel Mossi
AU - Nicole Mazzitelli Narvaz
AU - Larissa da Silva Bento
AU - Roger Modena
AU - Bernardo Boatini
AU - Amalia Garcez
AU - Leonardo Gregory Brunnet
AU - Alexandre Arenzon
AU - Carolina Brito
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.25403
UR - https://arxiv.org/abs/2609.25403
AB - The motility of microorganisms provides a natural framework for studying active matter and its response to environmental perturbations. We experimentally investigate the trajectories of \textit{Paramecium caudatum} and \textit{Artemia salina} under controlled conditions. Using video microscopy, we reconstruct individual trajectories over time. Beyond baseline conditions, we analyze stressed environments where \textit{P. caudatum} are exposed to a toxic agent and \textit{A. salina} are placed in distilled water. To characterize the motion, we compute trajectory-based metrics, including mean square displacement, velocity distributions, persistence times, tortuosity, and radius of gyration. These observables quantify changes in motility patterns across species and conditions. To interpret the dynamics, we develop coarse-grained stochastic models parameterized directly from experimental data. \textit{P. caudatum} is described within a run-and-tumble framework, including reversal events under stress. In contrast, \textit{A. salina} requires a run-and-tumble-like description with persistent orientational noise, accounting for continuous directional fluctuations during runs. The models reproduce the main trajectory-level observables used for comparison, namely tortuosity and radius of gyration. This combined approach provides insight into how environmental stress modifies locomotion statistics. Our results highlight systematic differences in the statistical signatures of motion, suggesting that trajectory metrics may serve as indicators of physiological stress. This work provides an accessible framework for active matter experiments where individual-level statistics can be directly measured and modeled.
ER -