Information processing by slime molds

Frances Taschuk

May 5, 2008

Slime molds!

“Dog Vomit”

“Pretzel Slime Mold” (Hemitrichia serpula)

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

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

Information Processing

• “Intelligence” without a brain

• Constraints:– Absorb nutrients– Maintain intracellular communication (remain

connected)– Limit body mass

Efficient Pathfinding?

1.Grow Physarum on agar (forms plasmodium)

2.Add food sources (oats) at specific points

3.Wait & take pictures

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

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

Maze Solving

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

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

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

Evolution of network

• Positive feedback:

• Leads to:– Dead end cutting– Selection of solution path from other

possibilities

conductivity

flux

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

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

Explanation: biological oscillators

• Locomotion depends on sum of oscillations

• “Memorizes” periodicity

• Elements of brain function: memory and anticipation

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

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