A new study has brought science one step closer to a molecular-level understanding of how patterns form in living tissue. The researchers engineered bacteria that, when incubated and grown, exhibited stochastic Turing patterns: a 'lawn' of synthesized bacteria in a petri dish fluoresced an irregular pattern of red polka dots on a field of green.
The mechanism of pattern formation in living systems is of paramount interest to bioengineers seeking to develop living tissue in the laboratory. Engineered tissues would have countless potential medical applications, but in order to synthesize living tissues, scientists need to understand the genesis of pattern formation in living systems.
A new study by researchers has brought science one step closer to a molecular-level understanding of how patterns form in living tissue. The researchers engineered bacteria that, when incubated and grown, exhibited stochastic Turing patterns: a "lawn" of synthesized bacteria in a petri dish fluoresced an irregular pattern of red polka dots on a field of green.
The stochastic Turing model is driven by randomness
In the current study, the researchers demonstrated both experimentally and theoretically that Turing patterns do in fact occur in living tissues — but with a twist. Where the instability that generates the patterns in Turing's model is defined as a high diffusion ratio between two chemicals, an activator and an inhibitor, in this study, researchers demonstrate that it's actually randomness — which would in most experiments be considered background noise — that generates what Goldenfeld has coined a stochastic Turing pattern.
"The theory of stochastic Turing patterns doesn't require a great difference in speed between the prey and the predator, the activator and the inhibitor. They can be more or less the same, and you still get a pattern. But it won't be a regular pattern. It'll be disordered in some way."
The bioengineering experiments
The bacterial patterning experiments in this study were being performed around the same time Goldenfeld and Butler were developing their theory. The initial motivation for the in vivo study was to see whether bacteria could be engineered to produce a Turing instability. The researchers used synthetic biology to engineer bacteria, based on the activation-inhibition idea from Turing.
They injected the bacteria with genes that made the bacteria emit and receive two different molecules as signals. The researchers attached fluorescent reporters to the molecules, creating a system where they could view the on/off switch of the genetic circuits through their signaling molecules: the activator fluoresced red and the inhibitor green. The researchers observed that starting with a homogeneous film, the engineered bacteria formed red dots surrounded by a field of green after incubation for a period of time — but the bacteria formed irregular Turing patterns, like those predicted by the stochastic theory.
Validating the stochastic Turing theory
To test if the experiments really were described by the new theory took several years of work. K. Michael Martini, worked with Goldenfeld to build a very detailed stochastic model of what was going on in these synthetic pattern-forming gene circuits, computed the consequences, and then compared the theoretical predictions with what the bioengineers had seen in the petri dishes.
"To really prove that our stochastic patterns work — it was hard. We had a lot of predictions we had made that had to be verified in experiment," comments Goldenfeld. "Because the mathematics that describe these patterns have many parameters, we had to explore all of the effects of each. It involved a lot of searching in parameter space to reveal what was the mechanism of pattern formation. And there was necessarily a lot of interaction and collaboration with our engineering colleagues.
Goldenfeld affirms, "This is really the first proof of principle that you can engineer in vivo stochastic Turing patterns, though it's not simple. So now we know that this mechanism really can work, and that these fluctuations can drive patterns. Ultimately, bioengineers would like to use this type of technology to make novel tissues and new functional biological systems. Our study shows that you can do that in a regime where the classical Turing patterns couldn't be used."