information processing by slime molds frances taschuk may 5, 2008
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Information processing by slime molds
Frances Taschuk
May 5, 2008
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Slime molds!
“Dog Vomit”
“Pretzel Slime Mold” (Hemitrichia serpula)
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Eeeew! What is it?• Kingdom Protista
– True slime molds: Phylum Myxomycota
– Cellular slime molds: Phylum Acrasiomycota • True slime molds: nucleus replicates without dividing to
form multinucleated feeding mass
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Why study them?
• Single, giant, multinucleated cell– Up to 20 meters in diameter!
• Biological information processing– Cell integrates sensory information and develops
response– Solve maze– Minimal risk path– Robot control
• Phototactic and chemotactic• Easily motivated by oats
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Information Processing
• “Intelligence” without a brain
• Constraints:– Absorb nutrients– Maintain intracellular communication (remain
connected)– Limit body mass
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Efficient Pathfinding?
1.Grow Physarum on agar (forms plasmodium)
2.Add food sources (oats) at specific points
3.Wait & take pictures
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SMT and CYC
• SMT = Steiner’s minimum tree:
graph with least sum of edge lengths (NP-complete problem)• CYC = plasmodium forms cyclical network• Minimum tube length vs robustness
SMT-like i) SMT-like
ii) combination
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Different restraint: risk presented by light– Produces reactive oxygen when exposed to light
extension velocity slows– Physarum demonstrates negative phototaxis
In pictures d,e,f: upper part of agar is illuminated
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Maze Solving
Video: http://video.google.com/videoplay?docid=-5425792330054733444&q=physarum&ei=3ycaSOHuL52cqQLS_9TfAQ
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Physical principles
• Mathematical model: feedback between thickness of tube and flux through it– More flux leads to wider tube
• Cytoplasmic streaming driven by rhythmic contractions of organism produces sheer stress to organize tubes
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Mathematical model• Cytosol is “shuttled” back and forth through the tubes--
most of the slime mold’s mass is at the food sources
• Network of tubes “evolves” - conductivity D changes depending on flux through tube
Pressure difference between ends of tube
Viscosity of sol Length of tube
Radius of tube
Flux
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Evolution of network
• Positive feedback:
• Leads to:– Dead end cutting– Selection of solution path from other
possibilities
conductivity
flux
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Response to stimuli
• Cellular control of robots
• Cells have a lot of computational power—inefficient to emulate biological processing using a computer– Plasticity of living cells: brownian motion
explores state space; conformational state change allows for signalling
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Anticipation of events• Changes in growth rates at different
temperatures/humidities– Grow for a few hours, then periodically stimulate with
cooler and drier temperatures– Result: growth slows periodically even when not
stimulated
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Explanation: biological oscillators
• Locomotion depends on sum of oscillations
• “Memorizes” periodicity
• Elements of brain function: memory and anticipation
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What does all this mean?
• Parallel dynamics (movement of sol in different parts of protoplasm) lead to information processing - no central processing unit required– Biology takes advantage of this!
• Nonlinear dynamics (oscillators) could help explain how biological systems develop intelligent behavior for survival
• Information processing power of biological cells may make them more adaptable than conventionally programmed robots
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References• Nakagaki, T., Iima, M., Ueda, T., Nishiura, Y., Saigusa, T., Tero, A., Kobayashi, R., Showalter, K.
2007. Minimum-risk path finding by an adaptive amoebal network. Physical Review Letters 99.• Nakagaki, T., Kobayashi, R., Nishiura, Y., Ueda, T. 2004. Obtaining multiple separate food
sources: behavioural intelligence in the Physarum plasmodium. Proc. R. Soc. B. 271: 2305-2310.
• "Slime Molds," Microsoft® Encarta® Online Encyclopedia 2007• Tero, A., Kobayashi, R., Nakagaki, T. 2007. A mathematical model for adaptive transport
network in path finding by true slime mold. Journal of Theoretical Biology 244: 553-564.• Tero, A., Nakagaki, T. 2008. Amoebae anticipate periodic events. Physical Review Letters 100:
018101.• Tsuda, S., Zauner, K-P., Gunji, Y-P. 2006. Robot control with biological cells. Biosystems 87:
215-223.• Photos:
– http://www.biology.duke.edu/dnhs/pics/SlimeMold.JPG– http://waynesword.palomar.edu/images/slime2b.jpg– http://researchfrontiers.uark.edu/6321.php– http://faculty.clintoncc.suny.edu/faculty/Michael.Gregory/files/Bio%20102/Bio%20102%20Laboratory/Protists/Physarum.JPG– http://bio.fsu.edu/~stevet/pictures/TheBigTree.jpg– http://io.uwinnipeg.ca/~simmons/16cm05/1116/28-29-PlasmSlimeMoldLife-L.gif