Ever wondered what causes the variety of patterns we see in nature — a zebra’s stripes, the leopard’s spots or the alternating arrangement of leaves on a plant’s branches? How do cells which are identical to start with, differentiate and form regular patterns? There indeed is a scientific explanation for it, and it has been now validated by experiments, too.
In their paper, recently published in the Proceedings of National Academy of Sciences, Nathan Tompkins and others describe an experiment involving a reaction-diffusion system which evolves patterns that can lead to cell differentiation.
Theoretically this was worked out nearly 60 years ago —Alan Turing came up with an explanation for the origin of static patterns in nature in 1952. In a seminal paper he outlined that chemicals diffusing across identical cells and interacting with each other would result in the cells developing differences in chemical concentrations and this would lead to their getting differentiated further. For instance, when sugar is dissolved in water, it spreads evenly through the water until every cell has equal concentration of sugar.
But if there are two species of chemicals diffusing through, an activator and inhibitor, for instance, what would happen is quite different. Let us say, this happens in a linear array of cells. There would, after some time, be a different quantity of each species of chemical in the different cells. This would be further complicated if the chemicals interacted with each other.
Using differential equations to describe this reaction-diffusion process, Turing predicted that after some time, there could emerge six patterns, some of which he identified with existing patterns in biology. To prove this, Dr. Tompkins model this process in the lab and show that their experimental system replicates five of these patterns and a further throws up a seventh, hitherto unpredicted pattern.
When asked how, given that they are studying small numbers of cells, they can be sure that the patterns they are seeing are indeed genuinely periodic patterns, Seth Fraden, Department of Physics, Brandeis University, a member of the collaboration, says in an email: “We use small numbers of drops in rings, but we also used long linear arrays in the order of 100 drops and even larger hexagonal, two-dimensional arrays of the order of 10,000 drops… We believe the patterns are genuine because they appear where the model predicts them.”