Collective Behaviors

Collective behaviour is a natural phenomenon involving numerous organisms synchronising their movements to form fascinating structures. These structures are widely observed in nature in humans, insects, birds, fish, microorganisms, etc., and even in the group of non-living entities like Janus particles. Owing to the diverse subject matter, it is an interdisciplinary endeavour that branches out into macro and microbiology, active and soft matter, swarm robotics, and crowd dynamics, to name a few. In our lab, we focus on the topics related to microbiology and active matter and strive to emulate the motion of swimmers in a medium.

Active matter is a broad term referring to any artificial/natural entity that has the ability to propel. When moving in tandem, these entities give rise to highly dynamical systems. Based on the hyperparameters of the setup, the entities can undergo a phase transition from a disorderly state to an orderly state. We have an in-house code involving a force-based interpretation of the Vicsek model to simulate the behaviour of self-propelling entities (active matter). In the presence of competing forces, we have observed novel organisation among these entities and detected mill formations, phase transitions the presence of symmetry breaking phenomena such as weak chimera. We also explore the effect of heterogeneity in the size and activity of the entities on the behavioural features of the system and study the hyperparameters that can be used to control mixing or segregation in such cases. Another novel phenomenon identified in our work is the relationship between collective motion and segregation. This can be of potential use in performing microfluidic tasks such as cell sorting.

The origin of collective behaviour in nature, albeit still unknown, can be attributed to a plethora of reasons such as easing the burden of foraging, increasing the alertness towards a predatory disruption, reducing energy consumption for locomotion, among others. We focus on the predation aspect of the origin story and have enabled our modular in-house code to handle the antagonistic interactions between multiple members of different species. The code is constantly under development to emulate the situation as realistically as possible with the aim to use it for a drug delivery scenario. We are also currently investigating the prospects of reinforcement learning to optimise the hunting strategies of the predator species.