Collective dynamics of soft active particles

TL;DR

This paper introduces a model of soft active particles that captures diverse collective behaviors observed in biological swarms, highlighting how local interactions influence swarm dynamics and efficiency.

Contribution

The study presents a novel model incorporating local interactions that reproduces various swarm behaviors and reveals how collective motion enhances search efficiency.

Findings

Different swarm states like migrating, rotating, and jammed behaviors identified.

Collective motion can significantly increase diffusion compared to individual particles.

Swarm dynamics depend on the relative strength of local interactions.

Abstract

We present a model of soft active particles that leads to a rich array of collective behavior found also in dense biological swarms of bacteria and other unicellular organisms. Our model uses only local interactions, such as Vicsek-type nearest neighbor alignment, short-range repulsion, and a local boundary term. Changing the relative strength of these interactions leads to migrating swarms, rotating swarms and jammed swarms, as well as swarms that exhibit run-and-tumble motion, alternating between migration and either rotating or jammed states. Interestingly, although a migrating swarm moves slower than an individual particle, the diffusion constant can be up to three orders of magnitude larger, suggesting that collective motion can be highly advantageous, for example, when searching for food.