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Artificial life
Pattern formation

Pattern formation

Another interesting point is how biological patterns (like we see them on the skin of zebras, gepards, snakes ...) can be explained. Those patterns are in most cases unique for each individual but they are still very similar (typical) for the specific species. I could imagine at least 2 possible mechanisms:

  • Genetically store all possible patterns in the genome (This way, all final patterns will be stored)
  • Genetically store the information about the components of a dynamic process, that create the pattern. (This way, only the process but not the final pattern will be stored)

The second way (not an invention of mine, it was Alain Turing, a genius scientist who invented this theory around 1952) would have many advantages:

  • There is not too much information to be stored
  • With a certain portion of stochasicity (as it’s always present in chemical reaction and in diffusion processes), the patterns will be unique for each individual, but still self-similar.

I will present a set of simulation on this page concerning these topics:

  • The first one is a full-featured simulation of a Turing-like reaction-diffusion process in Star Logo (click here).
  • Some of my extensions of the pattern-formation simulation from Scott Camazines home page will follow soon.

Further readings:

  • Camazine S., Deneubourg J.-L., Franks N.R., Sneyd J., Theraulaz G. and Bonabeau E. (2001) Self-Organization in biological systems. Princeton University Press.
  • Bonabeau E. (1997) From classical models of morphogenesis to agent-based models of pattern formation. Artificial Life 3:191-211
  • Edelstein-Keshet L. (1988) Mathematical models in biology. Birkhäuser mathematics series. McGraw-Hill

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