ArticleProceedings of the National Academy of Sciences of the United States of America2021
Microswimmers learning chemotaxis with genetic algorithms.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Heuristically Adaptive Diffusion-Model Evolutionary Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Run-and-tumble particles learning chemotaxis.Soft matter · 2025Article
- Learning to Navigate in Chemical Fields Without A Map at Low Reynolds Numbers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Reinforcement learning of a biflagellate model microswimmer.The European physical journal. E, Soft matter · 2025Article
- Chemotactic navigation in robotic swimmers via reset-free hierarchical reinforcement learning.Nature communications · 2025Article
- Reinforcement learning of biomimetic navigation: a model problem for sperm chemotaxis.The European physical journal. E, Soft matter · 2024Article
- Learning optimal integration of spatial and temporal information in noisy chemotaxis.PNAS nexus · 2024Article
- Smart active particles learn and transcend bacterial foraging strategies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Learning how to find targets in the micro-world: the case of intermittent active Brownian particles.Soft matter · 2024Article
- Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination.Scientific reports · 2023Article
- Biodegradable Microrobots and Their Biomedical Applications: A Review.Nanomaterials (Basel, Switzerland) · 2023Review
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4 authors.
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Abstract
Various microorganisms and some mammalian cells are able to swim in viscous fluids by performing nonreciprocal body deformations, such as rotating attached flagella or by distorting their entire body. In order to perform chemotaxis (i.e., to move toward and to stay at high concentrations of nutrients), they adapt their swimming gaits in a nontrivial manner. Here, we propose a computational model, which features autonomous shape adaptation of microswimmers moving in one dimension toward high field concentrations. As an internal decision-making machinery, we use artificial neural networks, which control the motion of the microswimmer. We present two methods to measure chemical gradients, spatial and temporal sensing, as known for swimming mammalian cells and bacteria, respectively. Using the genetic algorithm NeuroEvolution of Augmenting Topologies, surprisingly simple neural networks evolve. These networks control the shape deformations of the microswimmers and allow them to navigate in static and complex time-dependent chemical environments. By introducing noisy signal transmission in the neural network, the well-known biased run-and-tumble motion emerges. Our work demonstrates that the evolution of a simple and interpretable internal decision-making machinery coupled to the environment allows navigation in diverse chemical landscapes. These findings are of relevance for intracellular biochemical sensing mechanisms of single cells or for the simple nervous system of small multicellular organisms such as
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