Statistical Modeling of a Distributed Network of Bacteria-propelled Microrobots (BacteriaBots)

Afforded by the small size and distributed nature of multi-agent microrobotic systems, microrobotics offers powerful solutions to a host of pervasive problems, from bioremediation to biosensing to medicine. Of particular interest are bio-hybrid microrobot approaches, which are based on combining synthetic micro/nano-scale components with engineered biological cells for locomotion, energy transduction, sensing, communication, and control. This intrinsically leads to complex stochastic behavior, particularly when several cells are integrated with one microscale object. In this work, we describe the stochastic dynamics of a distributed network of BacteriaBots, microparticles decorated with engineered E. coli bacteria with the capacity for directed migration (chemotaxis) and chemical cell-cell communication (quorum sensing, QS). We developed a statistical model of BacteriaBot motility and chemotaxis which was integrated with an agent-based computational platform to accurately capture their spatiotemporal migration and QS functionality. We show that BacteriaBots have the capacity to significantly outperform analogous networks of free-swimming bacteria in QS-based task completion due to the locally high concentrations of bacteria in each microrobot. Our results demonstrate the utility of BacteriaBots as a programmable multi-agent system, and our model serves to underpin the rational design of such bio-hybrid systems toward specific applications.