Microswimmers learning chemotaxis with genetic algorithms

Significance Swimming microorganisms and migrating cells have developed various strategies in order to move in nutrient-rich or other chemical environments. We apply a genetic algorithm to the internal decision-making machinery of a model microswimmer and show how it learns to approach nutrients in static and dynamic environments. Strikingly, the emerging dynamics resembles the well-known run-and-tumble motion of swimming cells. We demonstrate that complex locomotion and navigation strategies in chemical environments can be achieved by developing a surprisingly simple internal machinery, which in our case, is represented by a small artificial neural network. Our findings shed light on how small organisms have developed the capability to conduct environment-dependent tasks. 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.