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The remarkably designed ornate boxfish doesn’t lack for detail when it comes to its hexagonal points and sharp lines — complex markings that are among the sharpest of the species, engineers at the University of Colorado Boulder had. How did this unique look come about?
Alan Turing, the famous mathematician who invented modern computing, proposed over 70 years ago that animals got their shapes through the production of chemical agents that spread through skin tissue, like creamer does in coffee. When chemicals come into contact with other agents, they inhibit their activity, resulting in pattern formation. But Turing’s theory doesn’t explain how patterns are defined in a species like the ornate catfish.
A team of engineers at the University of Colorado Boulder investigated how a mechanism called diffusiophoresis can create sharp shapes in a newly published study. Wednesday in the journal Science Advances. Diffusiophoresis describes the movement of molecules suspended in a liquid concentration gradient A single chemical causes small particles, in this case chromatophores (pigment cells), to focus and clump together.
When scientists calculated Turing’s equation and modified it to include this process, the simulations they created showed that the path of the molecules always formed sharp outlines, unlike the fuzzy, ill-defined points that Turing’s theory alone produces.
“What we were really interested in is that if it’s widespread, the patterns shouldn’t be as sharp … the colors shouldn’t be too noticeable,” said the study co-author. Ankur Gupta, assistant professor of chemical and biological engineering at the University of Colorado Boulder. “So, what’s that strike for these patterns? That’s where diffusiophoresis comes in.
Engineers’ findings suggest that chemical agents accompany the spread of chromatophores In diffusiophoresis they are dragged along their path, forming dots and lines with a very sharp outline, According to a news release On the course.
Gupta said he hopes the findings will inspire more research into diffusiophoresis related to embryogenesis and tumorigenesis and camouflage, as well as biological processes in other organisms.
“The idea of sharpening the interfaces is good, and certainly important for biological activity,” he said Dr. Andrew CrossAssistant Professor of Applied Mathematics at Durham University, United Kingdom Studied Turing TheoryIn email.
“Mathematical concepts like diffusion often lead to ‘smooth’ or continuous interfaces, whereas most boundaries in biological tissues (e.g., the boundaries between your organs) are relatively hard. … This is at least one possible way to sharpen regions of gene expression,” the study said. said a noncommittal Cross.
Turing’s hypothesis first appeared in a paper he wrote in 1952 entitled “The Chemical Basis of Morphogenesis”. His theory argued that animal patterns are not random, but rather a chemical reaction-diffusion process that systematically produces spots on a leopard or stripes on a tiger. University of Warwick.
While diffusiophoresis is a suggested modification to sharpen Turing’s theory based on recent research, there may be other possible solutions, he said. Jeremy GreenProfessor of Developmental Biology at King’s College London.
“Cells are very sticky and very unlikely to be moved by diffusiophoresis,” Green, who was not involved in the study, said in an email. “Moving cells to sharpen a Turing shape (or indeed any boundary) is not a new idea, and can occur by other mechanisms besides chemotaxis (active cell migration).”
Green said he hopes the study will influence modeling and experimentation in the future, but there are still gaps in Turing’s theory. Green Associate Editor A February 2012 survey Evidence was found to support Turing’s theory.
“We considered other possibilities in our paper and agreed that processes such as chemotaxis, i.e. cell migration, exist,” Gupta said in an email. “We don’t want to say that diffusiophoresis is the only mechanism, but rather that it exists and is underappreciated. Including diffusiophoresis helps improve the robustness of such predictions.”
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