Molecular Systems Biology · Published 2026-07-16 · DOI 10.1038/s44320-026-00232-7
Marian Shaw, Sadhya Garg, Julia Kirby, Samuel Schmidgall, Filippo Liguori, Anjali Doshi, Tao Tu, Tal Danino
Abstract Many bacterial species form self-organized macroscale patterns through swarming. Despite its extensive genetic tractability, Escherichia coli remains underexplored for robust, applied control of swarming. Here we develop a set of E. coli strains that generate centimeter-scale swarming patterns to spatially record environmental inputs. Specifically, we modulate the expression of swarming-related genes in response to chemical and optical signals, reshaping baseline swarm patterns in analog or binary-like fashions. To decode bacterial patterns across space and time, we develop scalable computational methods incorporating feature extraction, regression, and deep-learning models. Time-lapse imaging reveals that colonies record inputs dynamically, enabling early-stage classification. This work establishes a strategy for spatial information recording in E. coli and expands the toolkit for programming emergent microbial behaviors at macroscopic scales.
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Shaw, M., Garg, S., Kirby, J., et al. (2026). Engineered E. coli swarming for binary and analog input recording. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00232-7