Engineered bacterial swarm patterns as spatial records of environmental inputs

Abstract

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility--a highly coordinated, rapid movement of bacteria powered by flagella1-5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to "write" external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from endpoint images using a segmentation model. This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.