An electrophysiological and kinematic model of Paramecium, the “swimming neuron”

Irene Elices (1) , Anirudh Kulkarni (1) , Nicolas Escoubet (2) , Léa-Laetitia Pontani (3) , Alexis Michel Prevost (3) , Romain Brette (1)

1 Institut de la Vision
2 LBD - Laboratoire de Biologie du Développement [IBPS]
3 LJP - Laboratoire Jean Perrin

Paramecium is a large unicellular organism that swims in fresh water using cilia. When stimulated by various means (mechanically, chemically, optically, thermally), it often swims backward then turns and swims forward again in a new direction: this is called the avoiding reaction. This reaction is triggered by a calcium-based action potential. For this reason, several authors have called Paramecium the “swimming neuron”. Here we present an empirically constrained model of its action potential based on electrophysiology experiments on live immobilized paramecia, together with simultaneous measurement of ciliary beating using particle image velocimetry. Using these measurements and additional behavioral measurements of free swimming, we extend the electrophysiological model by coupling calcium concentration to kinematic parameters, turning it into a swimming model. In this way, we obtain a model of autonomously behaving Paramecium . Finally, we demonstrate how the modeled organism interacts with an environment, can follow gradients and display collective behavior. This work provides a modeling basis for investigating the physiological basis of autonomous behavior of Paramecium in ecological environments.

Domaines: Microbiologie et Parasitologie

Irene Elices, Anirudh Kulkarni, Nicolas Escoubet, Léa-Laetitia Pontani, Alexis Michel Prevost, et al.. An electrophysiological and kinematic model of Paramecium, the “swimming neuron”. PLoS Computational Biology, 2023, 19 (2), pp.e1010899. ⟨10.1371/journal.pcbi.1010899⟩. ⟨hal-04237420⟩