Biofilm is a self-assembling microbial community that can serve as a model system for studying emergent collective dynamics of living systems. Bacillus subtilis biofilms resolve a conflict between interior and peripheral cells by electrical signaling and oscillatory colony-growth dynamics. Intriguingly, this dynamics maintain the interior-cell populations within a biofilm, which ultimately improves the survivability against antibacterial treatments as a whole. Beyond intra-biofilm coordination, two biofilms in a microfluidic device can coordinate their oscillatory phases according to the nutrient availability, through which biofilms improve their survivability by effectively utilizing limited resources. While models that can separately simulate intra- and inter- biofilm oscillatory dynamics have been proposed, recapturing these dynamics by a simple phenomenological model remains to be done. Extending from our previous work where we developed a simple reaction-diffusion model that captures the essence of the oscillatory colony growth dynamics of a single biofilm, here we show that a model similar to our previous one can recapitulate the inter- as well as intra-biofilm dynamics.

One-dimensional crawling locomotion, which is employed by creatures such as earthworms and slugs, has garnered the attention of roboticists because it can be used to negotiate narrow spaces for propulsion. However, the previously developed robots are not adaptive enough to the environment. To address this problem, we have recently proposed a simple decentralized control scheme, and showed through simulations that it can adapt to the inclination angle of the terrain. In this study, we demonstrate that a one-dimensional crawling robot that implements a control scheme slightly modified from the previous one can move adaptively in the real world.

We propose a simple decentralized control scheme for swarm robots that can perform spatially distributed tasks in parallel, drawing inspiration from the non-reciprocal-interaction-based (NRIB) model we proposed previously. Each agent has an internal state called “workload.” Each agent first moves randomly to find a task. When it finds a task, its workload increases, and then it attracts its neighboring agents to ask for their help. Simulation was used to demonstrate the validity of the proposed control scheme.

Collective dynamics is a behavior of living systems that can improve their survivability in harsh and complex environments. Towards improving the vulnerability of engineering systems against power-source limitations, we focused on an oscillatory-growth dynamics of Bacillus subtilis biofilms. We developed a minimal reaction-diffusion model that captures the essence of the bacterial growth, nutrient consumption and electrical signalling. Numerical simulation of the model successfully recapitulated the oscillatory dynamics of bacterial biofilms. Thus, our model provides a first step forward towards designing biofilm-inspired engineering systems such as swarm robots and power supply networks.

Altruistic behaviors, such as self-sacrifices, are commonly observed in diverse living systems from bacteria to animal societies. Motivated by the fact that self-sacrifices of individuals can benefit the entire populations, we developed a decentralized control scheme with self-sacrifice by extending the Slimebot model. When an agent is not performing favorably, the agent self-sacrifices by stopping the motion and transferring its energy to nearby agents. We demonstrate via simulations that the proposed control scheme enables the agents to perform tasks effectively under several environments.

The collective behaviour of individuals is widely observed in many natural and social systems. In these systems, Newton’s third law, or the law of action–reaction, is often violated. Hence, interaction between individuals is often nonreciprocal. Several previous studies focused on and partially elucidated the mechanism of the aforementioned systems. In this study, we aim to further deepen the understanding from a general perspective by proposing and analysing a simple mathematical model. The model is proposed by drawing inspiration from friendship formation in human society. It is demonstrated via simulations that various patterns emerge by changing the parameters. Further, these patterns are characterized by two macroscopic variables, based on which they are classified into six categories. Through this classification, we found that lifelike complex patterns emerge at the boundary between the parameter spaces where relative position among particles are fixed and where some particles move infinite distance from the others. Although this study is still in a rudimentary stage, we believe that our finding in which macroscopic patterns are related to local rules could move one step forward in understanding the core principle of collective behaviour.