Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 19, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2019683118
Keywords
low-Reynolds number swimming; chemotaxis; machine learning; neural network; genetic algorithm
Categories
Funding
- Austrian Academy of Sciences
- ECAM, an einfrastructure center of excellence for software, training, and consultancy in simulation and modeling - European Union Project [676531]
- Austrian Science Fund (FWF) [M 2458-N36, I3846]
- Austrian Science Fund (FWF) [I3846] Funding Source: Austrian Science Fund (FWF)
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This study introduces a computational model where microswimmers can autonomously navigate in diverse chemical environments and control their shape deformations. Simple neural networks evolve to control the behavior of the microswimmers through the use of an evolutionary algorithm.
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 Caenorhabditis elegans.
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