Tag Archives: math models

Turing Patterns: What do a leopard’s spots, vegetation in arid zones, and the formation of fingers have in common?

Please excuse my inconsistent posting of late, I have been deep down the rabbit hole of science. Last week, I attended the Society of Industrial and Applied Math (SIAM) dynamical systems conference. What fun!

I learned about Turing Patterns, named for mathematician Alan Turing. Complex patterns can arise from the balance between the diffusion of chemicals and the reaction of those chemicals. For this reason, Turing’s model is also called the Reaction-Diffusion model. In general, these kinds of patterns can arise when there’s some kind of competition.

This sounds abstract, but suspected examples in nature abound. Have you ever wondered how the leopard got his spots or what’s behind the patterns on seashells? We often don’t know the chemical mechanisms that produce the patterns, but we can mathematically reproduce them with generic models.

Image from wired.com discussion of Turing patterns.

Mary Silber and her grad student Karna Gowda presented research on Turing patterns in the vegetation of arid regions. When there isn’t enough precipitation to support uniform vegetation, what vegetation will you observe? If there’s too little water, their model yields a vegetation-free desert. Between “not enough” and “plenty” the model generates patterns, from spots to labyrinths to gaps. Their work expands at least two decades worth of study of Turing patterns in vegetation.

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Figure by Karna Gowda, see the full article at SIAM news.

Silber and Gowda considered an area in the Horn of Africa (the bit that juts east below the Middle East). Here, stable patterns in the vegetation have been documented since the 1950s. They wanted to know how the patterns have changed with time. Have the wavelengths between vegetation bands changed? Are there signs of distress due to climate change? By comparing pictures taken by the RAF in the 1950s to recent satellite images, they found that the pattern were remarkably stable. The bands slowly travelled uphill, but they had the same wavelength and the same pattern. They only observed damage in areas with lots of new roads.

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From google maps of the Horn of Africa! I screen-capped this from here.

Turing patterns have even been studied experimentally in zebrafish. Zebrafish stripes might appear stationary, but they will slowly change in response to perturbations. So scientists did just. Below is a figure from the paper. The left shows the pattern on the zebrafish, the right shows the predictions of the model.

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Experimental perturbations to the patterns of zebrafish are well-predicted by the Turing model. Read more in this excellent Science paper.

The model has been used to explain the distribution of feather buds in chicks and hair follicles in mice. Turing’s equations have even been used to explain how fingers form.

If you want to learn more, the links above are a great start. And if you want to play with the patterns yourself, check out this super fun interactive. These waves aren’t stationary like the Turing patterns I described here, but they arise from similar mathematics. The interactive can make your computer work, fyi.

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Reaction-diffusion pattern I generated with this online interactive. It’s super fun!

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Happy 50th Anniversary, Chaos

This month, the American Physics Society magazine, Physics Today, published an article about the 50th anniversary of the Lorenz model. At the link, you can read the entire article. In it, experts describe the history of chaos, Lorenz’s discovery of it, and some of the state of the field today, but with a great deal less technical jargon.

50 years ago, Edward Lorenz first captured the mathematical phenomena we now know as chaos, known popularly as the “butterfly effect“. Below is a picture from the Lorenz model exhibiting chaos. The idea of chaos boils down to highly structured behavior that cannot be predicted. No matter how precisely we measure, after some time we cannot know the state of the system. We can say that the system will stay in a certain region of weather; in the picture below, there are definitely places the trajectory does not visit. We observe this with weather models– the forecast is good for a couple of days, so-so for a couple of days after that, and completely inaccurate for any time farther in the future. Analogously, we can say that it will not be -100 C tomorrow. Appropriately, Lorenz’s discovery of chaos came about as he tried to develop a model for the weather. Chaos is all around us and can be observed in a number of systems.

the Lorenz system, which turned 50 this year

At this link, you can play with a fun Lorenz model java applet. The trick with the applet is choosing the right parameters. Try setting the “spread” to 0.1, the “variation” to 20, the “number of series” to 2, and the “refresh period” to 100. Then push the button “reset the parameters” and “restart”. This will start 2 trajectories in the Lorenz model that differ by only 0.1. You will quickly see the two paths diverge and become completely unrelated. If you reduce the “spread” to 0.01, the same thing will happen, though it will take longer. As long as the spread is more than 0, the two paths will eventually diverge.

This is why we cannot predict the state of a chaotic system, because our ability to measure the state of the system is inevitably flawed. If we could measure the state of the weather to 99.99999% accuracy, that 0.00001% inaccuracy would eventually lead to divergence. And you can imagine that getting 99.99999% accuracy is much harder and more expensive than 99.9% accuracy.

Did you know that Pluto’s orbit is chaotic? Or a double pendulum? Or the logistic model for population dynamics? So check out the Lorenz model, and happy chaos-ing.