IGEM:Cambridge/2008/Notebook/Turing Pattern Formation
|Customize your entry pages|
We are planning to implement a simple two-component Reaction-Diffusion system in the gram-positive model organism Bacillus subtilis. In 1952, Alan Turing famously described this system and suggested it as the basis for self-organization and pattern formation in biological systems. The simplest of these patterns, which we are planning to model in bacteria, mimic the spots and stripes seen on animal coats.
(A) The model consists of two diffusible signals secreted by every cell. The activator, which is controlled by a stochastic bistable switch, turns on itself and its own inhibitor. (B) A field of cells can be stably patterned into two different zones, so long as the inhibitor diffuses faster than the activator. The activator and inhibitor are synthesized in the source at the center, and turned off by accumulation of the inhibitor in the periphery.
We plan to use two well-characterized bacterial communication systems to generate this behavior. The agr peptide signalling system from S. aureus will serve as our activatory signal (pictured), while the lux system from V. fischeri will serve as our inhibitor. Bacillus subtilis serves as an excellent chassis for this project because of the ease with which chromosomal integration can be performed. This project will focus on a tight integration of modeling and experiment; we will test different promoter strengths and other variables, feed these system parameters into our multi-cell models, and then use those models to tweak the regulatory machinery that will control signal production.
The reaction-diffusion system depends on an activator and inhibitory signal that spread throughout the medium. The "grasshopper" example is quite intuitive: Imagine it is hot and there is a field of dry grass with grasshoppers. Suddenly, a fire starts burning at some point and spreads (the activator signal) so that the grasshoppers move away from that point to avoid the fire. However, the grasshoppers also generate moisture (the inhibitory signal) thus preventing the areas of dry grass the grasshoppers move to of catching fire. The result will be the initial patch of the field that has burnt down surrounded by moisture preventing the fire from spreading. Imagine now that at the beginning, not a single place but numerous randomly distributed places (resembling noise) of dry grass caught fire. The resulting patterning of charred grass and grasshoppers is called a Turing Pattern. It is important to note is that the inhibitory signal (grasshoppers) must travel faster than the activation signal (fire) as to prevent the whole field from burning down.
This project seeks to generate Turing Patterns by creating a Reaction-Diffusion system in the gram-positive bacteria Bacillus subtilis. We need to integrate two signalling systems into this bacterium and use an autofeedback mechanism to generate self-organizing patterns from random noise. We plan to incorporate the agr peptide signalling system from S. aureus and the lux AHL system from V. fisheri.
Bacillus strain 1A1 (derivative of standard strain 168)
Bacillus strain 1A771 (derivative of standard strain 168)