Synthetic Biology

The Bacterial Version of Photoshop

Science Signaling  07 Jul 2009:
Vol. 2, Issue 78, pp. ec228
DOI: 10.1126/scisignal.278ec228

To take control of biological systems, Tabor et al. argue, we need the ability to predict the behavior of complex genetic programs, which are analogous in some ways to electronic circuits that allow logical operations in computers. One strategy to obtain such an understanding is to build such circuits and to observe and to model mathematically their behavior. Tabor et al. used a combination of simple genetic circuits in combination to build a program that allowed a layer of bacteria on a Petri dish to function as an “edge detector” sensitive to areas of transition between high and low illumination (light-dark). A photoreceptor provided light sensitivity and was coupled through a two-component regulatory system to control of a transcriptional promoter that regulated production of a dark pigment (though production of β-galactosidase). Instead of the brute force method your computer would use (in which each pixel of an image has to be compared for its intensity with that of its neighbors, a process that requires ever-larger computation time as the image size increases), Tabor et al. designed a massively parallel computation system involving large numbers of bacteria spatially distributed in a layer. This required communication between bacteria such that cells in the dark produced a diffusible signal (3-oxohexanoyl-homoserine lactone) that activates a transcription factor. By wiring the genetic circuit so that only cells that are both sensing light and receiving the diffusible signal from a neighboring cell in the dark produced pigment, the system reliably controlled pigment production to define the edges. This, the authors note, is the same sort of combination of logic functions and cell-cell communication that allows pattern formation in development. Building and understanding such simple systems is expected to both enhance our capacity to understand more complicated natural systems and to enhance our ability to engineer desired biological effects in living systems.

J. J. Tabor, H. M. Salis, Z. B. Simpson, A. A. Chevalier, A. Levskaya, E. M. Marcotte, C. A. Voight, A. D. Ellington. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009).[PubMed]