A Synthetic Genetic Edge Detection Program

Edge detection is a signal processing algorithm common in artificial intelligence and image
recognition programs. We have constructed a genetically encoded edge detection algorithm that
programs an isogenic community of E.coli to sense an image of light, communicate to identify the
light-dark edges, and visually present the result of the computation. The algorithm is implemented
using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light
and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions.
Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a
positive output. A mathematical model constructed from first principles and parameterized with
experimental measurements of the component circuits predicts the performance of the complete
program. Quantitatively accurate models will facilitate the engineering of more complex biological
behaviors and inform bottom-up studies of natural genetic regulatory networks.