MPI-DS scientists reveal a bacterial interaction that results in complex patterns and introduce a versatile model that can decode the collective behavior of entities ranging from bacteria to swarms of robots. A new model suggests that chasing interactions can induce dynamic patterns in the organization of bacterial species.
Structural patterns can be created due to chasing interactions between two bacterial species. In a new model, scientists from the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) describe how interactions at the individual level lead to the self-organization of species, and their findings provide insights into general mechanisms of collective behavior.
In a recent study, scientists from the Department of Physics of Living Matter at MPI-DS developed a model to describe communication pathways in bacterial populations. Bacteria exhibit overall organizational patterns by sensing the concentration of chemicals in their environment and adjusting their movements.
"We simulated a non-reciprocal interaction between two bacteria," explains first author YuDuan. "This means that species A is chasing species B, and species B's goal is to repel species A." The researchers found that this chase and avoidance interaction alone was enough to form a structural pattern. The type of pattern generated depends on the intensity of the interaction. This complements a previous study in which a model was proposed that also included intraspecific interactions of bacteria to form a pattern.
Also included in this new model are the effects of bacterial movement, which does not require adhesion or alignment to form complex superstructures containing millions of individuals. "While bacterial population dynamics show overall order, this is not the case at the level of individual bacteria. In particular, individual bacteria seem to move in a disordered way, with a structure that is only visible at higher levels, which is very fascinating," concludes Benoît Mahault, Group Leader of the Department of Physics of Living Matter at MPI-DS.
The model also allows more than two species to be considered, increasing the number of possible interactions and emerging patterns. It’s worth noting that it’s not limited to bacteria either, and can be applied to a variety of collective behaviors. These include light-controlled microswimmers, social insects, animal swarms and robot swarms. This study therefore provides general insights into the mechanisms that form large-scale structures in networks with many components.