Study reveals how bacteria can organize themselves

study reveals how bacteria can organize themselves – The News Mill

ANI Photo | Study reveals how bacteria can organize themselves

In a recent study, scientists from MPI-DS’s department of Living Matter Physics built a model that describes ways to interact in bacterial communities. Bacteria exhibit a general organisational structure by sensing chemical concentrations in their environment and adapting their movement.
The structure only becomes visible on a higher level.
“We modeled the non-reciprocal interaction between two bacterial species”, first author Yu Duan explains. “This means that species A is chasing species B, whereas B is aiming to repel from A”, he continues. The researchers found, that just this chase-and-avoid interaction is sufficient to form a structural pattern. The type of the resulting pattern depends on the strength of the interaction. This complements a previous study, where a model was proposed that also included intraspecies interactions of the bacteria in order to form a pattern.
In this new model, which also includes the effect of bacterial motility, neither adhesion nor alignment are required to form complex super-structures encompassing millions of individuals. “Although the bacterial population dynamics show a global order, this is not the case on the individual bacterial level. In particular, a single bacterium seems to move in a disordered way, with the structure becoming visible only on a higher level, which is very fascinating”, summarizes Benoit Mahault, group leader in the department Living Matter Physics at MPI-DS.
A general model for collective behavior
The model also allows to consider more than two species, increasing the amount of possible interactions and emerging patterns. Notably, it is also not limited to bacteria but can be applied to a variety of collective behaviors. These include light-controlled microswimmers, social insects, animal groups and robotic swarms. The study therefore provides general insights on the mechanisms responsible for the formation of large-scale structures in networks with many components. (ANI)

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