Dense Networks of Bacteria Propelled Micro-Robotic Swarms

Abstract:

We are developing a new computational framework and physical platform for modeling, analyzing, and designing dense networks of micro-robotic swarms. The physical platform is based on a bio-hybrid micro-robotic approach, in which bacteria serve as on- board actuators. The micro-robots are controlled through passive (e.g. chemical gradients) and active (e.g. magnetic fields) steering mechanisms. Here, we present the first step towards passive control by characterizing the chemotactic behavior of free- swimming bacteria. We apply a full pathway model and experimental three-channel linear gradient platform for characterizing the chemotactic response of the bacterium, Serratia marcescens, to the chemoattractant, L-aspartate. The response is characterized over a large range of chemical gradients (1.0x10-3-5 mM/mm) and average concentrations (0.5x10-3-2.5 mM). We find that the bacteria can sense a wide range of gradients (3-4 decades), and the strongest chemotactic response occurs for a gradient of 0.2 mM/mm (0.1 mM in C ). The model predictions show good agreement with the experimental results. Through the use of our statistical physics model and physical platform, we work towards developing a formal stochastic framework to accurately characterize micro-robotic swarm dynamics, with the future goal of using this new Cyber Physical System design in medical diagnosis and targeted drug delivery applications.