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Related Experiment Video

Updated: Nov 6, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

Microswimmers learning chemotaxis with genetic algorithms.

Benedikt Hartl¹, Maximilian Hübl¹, Gerhard Kahl¹

¹Institute for Theoretical Physics, Technische Universität Wien, 1040 Wien, Austria.

Proceedings of the National Academy of Sciences of the United States of America

|May 5, 2021

Summary

This summary is machine-generated.

Microswimmers adapt their shape to navigate chemical gradients using artificial neural networks. This computational model reveals how simple decision-making machinery enables efficient chemotaxis in complex environments.

Keywords:

chemotaxis genetic algorithm low-Reynolds number swimming machine learning neural network

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Area of Science:

Biophysics
Computational Biology
Artificial Intelligence

Background:

Microorganisms and mammalian cells utilize nonreciprocal body deformations for locomotion in viscous fluids.
Chemotaxis, the directed movement towards nutrients, requires sophisticated adaptation of swimming gaits.

Purpose of the Study:

To develop a computational model for autonomous shape adaptation in microswimmers.
To investigate the role of artificial neural networks in controlling microswimmer navigation and chemotaxis.

Main Methods:

A computational model simulating microswimmers with shape adaptation controlled by artificial neural networks.
Implementation of spatial and temporal sensing mechanisms for gradient detection.
Utilizing the NeuroEvolution of Augmenting Topologies (NEAT) genetic algorithm for neural network evolution.

Main Results:

Evolved simple neural networks effectively control microswimmer shape for navigation in static and dynamic chemical environments.
Introduction of noise into the neural network successfully replicated the biased run-and-tumble motion observed in bacteria.
Demonstrated that interpretable decision-making machinery coupled with environmental sensing facilitates navigation.

Conclusions:

Artificial neural networks can evolve simple yet effective strategies for microswimmer navigation and chemotaxis.
The model provides insights into the evolution of biological sensing and decision-making mechanisms.
Findings are relevant for understanding intracellular sensing and simple nervous systems in organisms like *C. elegans*.