Engineered E. coli swarms record chemical and light inputs in visible patterns

Bacteria are famous for forming patterns, from the concentric rings of Proteus colonies to the branching networks that motile microbes paint across agar plates. Now researchers at Columbia University have turned one of biology’s most familiar workhorses, Escherichia coli, into a living recording device whose swarming colonies capture environmental information in centimeter-scale, visually readable patterns. Writing in Molecular Systems Biology, a team led by Marian Shaw, Sadhya Garg and Tal Danino describes a set of engineered E. coli strains that register chemical and optical inputs in either binary or analog form, and a computational toolkit that decodes those patterns with near-perfect accuracy.

Swarming is a collective form of surface motility in which bacteria elongate, hyperflagellate and coordinate their movement to spread rapidly across surfaces. As a colony expands, its morphology integrates cellular responses over space and time, effectively producing a visible record of the conditions the cells encountered during growth. The team reasoned that this emergent behavior could be hijacked for information recording, complementing DNA-based recorders that store data in genomic sequences and require sequencing to read out. Unlike molecular recorders, swarm patterns can be inspected with the naked eye or a cheap flatbed scanner.

The platform is anchored in a hypermotile derivative of the canonical E. coli K-12 strain MG1655, known as MG1655hm, which carries an insertion sequence upstream of the flhD promoter that boosts expression of the master flagellar regulator flhDC. On semi-solid agar supplemented with glucose, this strain forms reproducible, radially symmetric colonies spanning several centimeters within 24 hours. To make the patterns input-responsive, the researchers built high-copy plasmids in which an IPTG-inducible pLac promoter drives the expression of individual swarming-related genes, with green fluorescent protein serving as a negative control.

Four candidate genes were selected for their distinct positions in the motility regulatory hierarchy. The leucine-responsive regulatory protein lrp sits near the top, activating flhDC; fliA encodes the flagellum-specific sigma factor sigma-28 that governs late flagellar genes; rpoS encodes the stress sigma factor sigma-S, which indirectly represses flagellar expression; and cheZ encodes the chemotaxis phosphatase that terminates rotational signaling and promotes forward runs. Overexpression of lrp produced feather-like branching and larger colonies, fliA yielded smaller, denser colonies with jagged borders, and rpoS generated compact colonies with a dense inner ring and short protrusions. CheZ overexpression, by contrast, left colony morphology essentially unchanged.

When the team swept IPTG concentrations across 24-hour swarming assays, the strains fell into two functional classes. The pLac-lrp strain behaved like a threshold-based, binary recorder: below roughly 0.1 millimolar IPTG it produced the baseline colony, while above that concentration its swarming expanse jumped dramatically to cover the entire Petri dish, with step-like changes in density variation and low-frequency image features. The pLac-fliA and pLac-rpoS strains, in contrast, responded in a graded, analog-like fashion, with colony radius, texture, eccentricity and pixel intensity shifting progressively across wide inducer ranges. Control strains carrying only gfp showed no meaningful pattern changes.

To read the recorded information, the researchers developed a semi-automated image-analysis pipeline that segments colony scans, converts them into Cartesian, polar and Fourier representations, and extracts features of scale, shape, density and texture. Regression models fit to these features decoded IPTG levels with striking fidelity: the binary lrp model achieved 100 percent accuracy with an area under the ROC curve of 1, while the analog fliA and rpoS models reached five-class decoding accuracies of 94 and 78 percent, with AUCs of 1 and 0.96. Control models performed near chance, confirming that decoding success depended on genuine engineered responses.

The team also trained deep-learning classifiers on raw colony images using the SwinTransformer vision architecture, bypassing handcrafted feature extraction entirely. These models matched or approached the regression results, with the lrp model again achieving perfect binary classification and the fliA and rpoS models reaching accuracies of 92 and 82 percent. Most notably, a single unified model trained on pooled images from both analog strains simultaneously decoded strain identity and IPTG concentration across nine classes, achieving 93 percent accuracy and a macro-averaged AUC of 0.9986, demonstrating that multiplexed sensing does not come at the cost of decoding performance.

Because swarming is inherently dynamic, the researchers repurposed a commercial flatbed scanner placed inside a 37-degree incubator to capture images every ten minutes throughout colony development. Time-lapse analysis revealed that expansion rate and local textural uniformity recorded IPTG dose in real time, and statistical power analyses showed that the two analog strains could be distinguished from uninduced controls with near-total confidence as early as five hours into growth. The platform could even register transient events: when a single drop of concentrated IPTG was pipetted beside an expanding colony front at 12 hours, rpoS colonies developed a measurable asymmetry, with reduced expansion specifically on the inducer-facing side, while control colonies remained radially symmetric.

To demonstrate modularity beyond chemical induction, the team swapped the IPTG circuit for pDawn, a blue-light optogenetic system, placing fliA under the control of a light-activated promoter. Illuminated colonies became progressively smaller and denser with increasing blue-light irradiance, and the same image-analysis pipeline, applied without modification, quantified graded analog responses in minimum radius and density heterogeneity. The authors argue that this plug-and-play design, combined with E. coli’s vast genetic toolkit, could extend swarm-based recording to environmental monitoring, clinical biosensing, educational demonstrations and engineered living materials, turning one of biology’s oldest collective behaviors into a programmable, macroscale readout of the microbial world.

Subject of Research: Engineering E. coli swarming motility to spatially record chemical and optical inputs in decodable colony patterns

Article Title: Engineered E. coli swarming for binary and analog input recording

Article References: Shaw, M., Garg, S., Kirby, J., Schmidgall, S., Liguori, F., Doshi, A., Tu, T., & Danino, T. (2026). Engineered E. coli swarming for binary and analog input recording. Molecular Systems Biology, 22(9), 1377-1400. https://doi.org/10.1038/s44320-026-00232-7

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00232-7

Keywords: E. coli, swarming motility, synthetic biology, biosensors, optogenetics, pattern formation, machine learning, image analysis, information recording, bacterial colonies, genetic circuits, Molecular Systems Biology