Minimum-Risk Path Finding by an Adaptive Amoebal Network

Abstract

When two food sources are presented to the slime mold Physarum in the dark, a thick tube for absorbing nutrients is formed that connects the food sources through the shortest route. When the lightavoiding organism is partially illuminated, however, the tube connecting the food sources follows a different route. Defining risk as the experimentally measurable rate of light-avoiding movement, the minimum-risk path is exhibited by the organism, determined by integrating along the path. A model for an adaptive-tube network is presented that is in good agreement with the experimental observations. Introduction.-The plasmodium of Physarum polycephalum is an amoebalike organism with a body made up of a tubular network through which nutrients, signals, and body mass are transported. Studies of this organism have shown that it is able to determine the shortest path through a maze as well as ''solve'' other geometric puzzles [1][2][3]. In a maze, a starved organism forms a tube that connects food sources (FS) placed at the two exits of the maze via the shortest path, while nearly the entire protoplasm of the amoeba gathers over the two FS. The organism meets its physiological requirements in adopting this shape by absorbing nutrients from the FS as rapidly as possible while maintaining sufficient connectivity to permit intracellular communication. Such behavior in a primitive organism of this kind may offer insights into the evolutionary origins of biological information processing.Here we give the plasmodium a new type of task involving optimization behavior. Two separate FS are presented to the organism, which is illuminated by an inhomogeneous light field. Because the plasmodium is photophobic, tubes connecting the FS do not follow the simple shortest paths but form according to the illumination inhomogeneity. We report on the behavior of the organism under these conditions and discuss its physiological significance. We also propose a mathematical model for the cell dynamics and present a computational algorithm for its problem solving.Organism and methods.-The plasmodium of Physarum polycephalum, which regenerated from the sclerotia in ca. one-half day in the dark (25 C), was used in the experiments. A plastic film was placed onto a 1% agar gel, leaving a rectangular area (1 2 cm 2 ) of the gel uncovered. A few pieces (0:5 1 cm 2 ) of the regenerated plasmodium were placed in the rectangular area, and the preparation was placed in the dark for a few hours. The

Caging the Blob: Using a Slime Mold to Teach Concepts about Barriers that Constrain the Movement of Organisms

“…The plasmodium is a streaming packet of cytoplasm containing many nuclei. In the plasmodial stage, P. polycephalum exhibits an amoeboid type of movement and is known to respond to a variety of environmental stimuli, including light, physical barriers, food sources, and chemical repellants (Nakagaki et al, 2000(Nakagaki et al, , 2007Adamatzky, 2010;Latty & Beekman, 2010). This response can be easily observed with the naked eye as the plasmodium grows, creating new pseudopodia and thickening tubes in some areas and shrinking back pseudopodia and allowing tubes to die in other areas (tubes that were once alive appear white rather than yellow).…”

“…As more materials are exchanged between two parts of a tube, the tube thickens and is reinforced. As fewer materials are exchanged, the tube thins and eventually dies (Nakagaki et al, 2007). Slime molds have been used in several diverse fields of scientific research.…”

Go Games on Plasmodia of Physarum Polycephalum

“…The behavior of plasmodia is very sensitive and intelligent [4], [5], [6], [7], [8], [15], [17], [18], [19]. This behavior can be represented as a bio-inspired game theory on plasmodia [14], i.e.…”

A Physarum-inspired approach to supply chain network design

“…Laboratory experiments and theoretical studies have shown that the slime mould can solve many graph theoretical problems, such as finding the shortest path [26][27][28][29][30][31][32][33], shortest path tree problem [34], network formulation and simulation [25,26,37], influential nodes identification [38], connecting different arrays of food sources in an efficient manner [39][40][41][42], network design [43][44][45][46][47][48].…”