Engineered bacterial swarm patterns as spatial records of environmental inputs

Abstract

AbstractA 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.