We demonstrate here that the true slime mold, a giant unicellular organism with multiple nuclei, is able to solve a maze and other geometrical puzzles, and how it solve the exercises based on pattern formation in spatially distributed biochemical oscillators.
The amoeboid body of the Physarum plasmodium contains a network of tubular elements by means of which nutrients and chemical signals circulate through the organism. When food pellets were presented at different points on the plasmodium it accumulated at each pellet with a few tubes connecting the plasmodial concentrations. The geometry of the network depended on the positions of the food sources. Statistical analysis showed that the network geometry met the multiple requirements of a smart network: short total length of tubes, close connections among all the branches (a small number of transit food-sites between any two food-sites) and tolerance of accidental disconnection of the tubes. These findings indicate that the plasmodium can achieve a better solution to the problem of network configuration than is provided by the shortest connection of Steiner¡Çs minimum tree, even though the Steiner's connection is really hard for human to obtain.
As above, this organism is very useful for studying the function and dynamics of natural adaptive networks. Some exercises we posed to the organism here concern optimal design of communication network in relation to social network of public transportation, life lines and so on. How does the organism obtain the smart solution? Two empirical rules describing changes in body shape are known: 1) tube of open ends are likely to disappear in the first step and 2) when two or more tubes connect the same two food sources, the longer tubes tend to disappear. These changes in the tubular structure are based on cell dynamics: Shuttle streaming of protoplasm, which is driven by rhythmic contraction, may affect the morphogenesis of tubular structures. Hence a key mechanism underlying network formation may involve the spatio-temporal dynamics of coupled oscillators on a network. We try to make mathematical model for the morphogenesis, and to clarify the biological algorithm of computing.
References:
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3. T.Nakagaki, et al. Maze-solving by an amoeboid organism, Nature 407 470 (2000).
4. T.Nakagaki, et al. Smart network solutions in an amoeboid organism, Biophys. Chem. 107 1-5 (2004).