1. Information processing by slime molds Frances Taschuk May 5, 2008

2. Slime molds! “Dog Vomit” “Pretzel Slime Mold” (Hemitrichia serpula)

3. 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

5. 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

6. Information Processing “Intelligence” without a brain Constraints: –Absorb nutrients –Maintain intracellular communication (remain connected) –Limit body mass

7. Efficient Pathfinding? 1.Grow Physarum on agar (forms plasmodium) 2.Add food sources (oats) at specific points 3.Wait & take pictures

8. 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

9. 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

10. Maze Solving Video: http://video.google.com/videoplay?docid=- 5425792330054733444&q=physarum&ei=3ycaSOHuL52cqQLS_9TfAQhttp://video.google.com/videoplay?docid=- 5425792330054733444&q=physarum&ei=3ycaSOHuL52cqQLS_9TfAQ

11. 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

12. 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 solLength of tube Radius of tube Flux

13. Evolution of network Positive feedback: Leads to: –Dead end cutting –Selection of solution path from other possibilities conductivity flux

14. 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

16. 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

17. Explanation: biological oscillators Locomotion depends on sum of oscillations “Memorizes” periodicity Elements of brain function: memory and anticipation

18. 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

19. 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.JPGhttp://www.biology.duke.edu/dnhs/pics/SlimeMold.JPG –http://waynesword.palomar.edu/images/slime2b.jpghttp://waynesword.palomar.edu/images/slime2b.jpg –http://researchfrontiers.uark.edu/6321.phphttp://researchfrontiers.uark.edu/6321.php –http://faculty.clintoncc.suny.edu/faculty/Michael.Gregory/files/Bio%20102/Bio%20102%20Laboratory/Protists/Physarum.JPGhttp://faculty.clintoncc.suny.edu/faculty/Michael.Gregory/files/Bio%20102/Bio%20102%20Laboratory/Protists/Physarum.JPG –http://bio.fsu.edu/~stevet/pictures/TheBigTree.jpghttp://bio.fsu.edu/~stevet/pictures/TheBigTree.jpg –http://io.uwinnipeg.ca/~simmons/16cm05/1116/28-29-PlasmSlimeMoldLife-L.gifhttp://io.uwinnipeg.ca/~simmons/16cm05/1116/28-29-PlasmSlimeMoldLife-L.gif