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. 2015 Aug 31;8(4):e1059007.
doi: 10.1080/19420889.2015.1059007. eCollection 2015 Jul-Aug.

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

Richard Mayne  1 , Andrew Adamatzky  1 , Jeff Jones  1
Affiliations

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

Richard Mayne et al. Commun Integr Biol. .

Abstract

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

Keywords: Physarum polycephalum; cytoskeleton; slime mold; unconventional computing.

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Figures

Figure 1.
Figure 1.
Plasmodium of slime mold P. polycephalum growing on an agar-filled Petri dish, feeding on porridge oats. Note the differences in morphology between medial/posterior plasmodial ‘veins’ (black arrow) and the ‘fan-shaped’ anterior margin (white arrow).
Figure 2.
Figure 2.
Simplified schematic representation of the cellular cytoskeleton in a generalized eukaryotic cell, illustrating how several varieties of cytoskeletal protein form an interconnected network which links cellular organelles together. Intermediate filaments and centrosome are not shown. Nu: nucleus; OM: outer membrane; MAP: microtubule-associated protein; MT: microtubule; ABP: actin-binding protein.
Figure 3.
Figure 3.
Low magnification confocal micrographs of 12μm sections of plasmodium taken from the advancing anterior margin, stained for cytoskeletal proteins. Note the dense, branching structure of each network and that they articulate onto each nucleus (nucleic acids stained blue). (A) Stained for actin with antibody ACTN(05) C4 (red). (B) Stained for tubulin with antibody KMX-1 (green). (C) Merge of figs. a + b with extended focus.
Figure 3.
Figure 3.
Figure 3.
Figure 3.
Figure 4.
Figure 4.
Higher magnification confocal micrographs of plasmodial sections stained for cytoskeletal proteins (same staining characteristics as Figure?3). (A) Actin network. (B) Tubulin network surrounding 2 isolated nuclei.
Figure 4.
Figure 4.
Figure 5.
Figure 5.
Figure 5.
Figure 5.
Figure 5.
Figure 5.
Figure 6.
Figure 6.
Schematic diagram of an f-actin strand where individual circles represent g-actin monomers. Cellular automaton rules are displayed for molecule xi (see text for rule description).
Figure 7.
Figure 7.
Actin automata evolving by rule C1(). Original configuration is a randomly excited nodes, where every node takes state + or − with probability 0.25. Only space-time configurations of chain x are shown. Time goes down. Excited nodes are shown by black pixels and resting nodes are white (no refractory nodes are present).
Figure 8.
Figure 8.
Actin automata evolving by rule C2(). Original configuration is a randomly excited nodes, where every node takes state + or − with probability 0.25. Only space-time configurations of chain x are shown. Time goes down. Excited nodes are shown by black pixels, refractory nodes by gray pixels and resting nodes are white.
Figure 9.
Figure 9.
Actin automata evolving by rule C3(). Original configuration is a randomly excited nodes, where every node takes state + or − with probability 0.25. (A) Space-time configurations of chain x, (B) Space-time configurations of chain y. Time goes down. Excited nodes are shown by black pixels, refractory nodes by gray pixels and resting nodes are white.
Figure 9.
Figure 9.
Figure 10.
Figure 10.
How a multi-agent model of slime mold outsources computation to the environment. (A) A small population (particle positions shown) of virtual plasmodium is inoculated on lowest node (bottom) and grows toward first node, engulfing it, and reducing chemoattractant projection, (B-D) Model population grows to nearest sources of chemoattractant completing construction of the spanning tree, (E-H) Vizualization of the changing chemoattractant gradient as the population engulfs and suppresses nutrient diffusion.
See this image and copyright information in PMC

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