Physarum Can Compute Shortest Paths

Bonifaci, Vincenzo; Mehlhorn, Kurt; Varma, Girish

doi:10.1137/1.9781611973099.21

Cited by 56 publications

(56 citation statements)

References 4 publications

(9 reference statements)

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“…This mecha- nism explains the experimental observation of a self-propagating increase in contraction amplitude across a body; moreover, the model solves the puzzle of how an individual can measure its own size and match the wavelength of pulsatile contractions to its size. The mechanism also reveals how P. polycephalum finds the shortest route through a maze (5) or connects multiple food sources with shortest routes into an efficient transport network (6,31). The signaling molecule remains unidentified but among the molecules known to oscillate in P. polycephalum (ATP, cAMP, H + , and Ca 2+ ), the most likely candidate is calcium.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the self-propagating amplitude fronts strengthen the shortest route between the two food sources by increasing the flow rate along it. The flow feedback-driven mechanism of signal propagation explains P. polycephalum's ability to find the shortest route through a maze (5) and its ability to link multiple food sources into an efficient transport network (6,31).…”

Section: Conservation Of Mass Allows Calculation Of Mean Concentrationmentioning

confidence: 99%

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Mechanism of signal propagation in Physarum polycephalum

Alim

Andrew

Pringle

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Complex behaviors are typically associated with animals, but the capacity to integrate information and function as a coordinated individual is also a ubiquitous but poorly understood feature of organisms such as slime molds and fungi. Plasmodial slime molds grow as networks and use flexible, undifferentiated body plans to forage for food. How an individual communicates across its network remains a puzzle, but Physarum polycephalum has emerged as a novel model used to explore emergent dynamics. Within P. polycephalum, cytoplasm is shuttled in a peristaltic wave driven by cross-sectional contractions of tubes. We first track P. polycephalum's response to a localized nutrient stimulus and observe a front of increased contraction. The front propagates with a velocity comparable to the flow-driven dispersion of particles. We build a mathematical model based on these data and in the aggregate experiments and model identify the mechanism of signal propagation across a body: The nutrient stimulus triggers the release of a signaling molecule. The molecule is advected by fluid flows but simultaneously hijacks flow generation by causing local increases in contraction amplitude as it travels. The molecule is initiating a feedback loop to enable its own movement. This mechanism explains previously puzzling phenomena, including the adaptation of the peristaltic wave to organism size and P. polycephalum's ability to find the shortest route between food sources. A simple feedback seems to give rise to P. polycephalum's complex behaviors, and the same mechanism is likely to function in the thousands of additional species with similar behaviors. acellular slime mold | transport network | behavior | Taylor dispersion O ne of the great challenges of unraveling biological complexity is understanding what kind of and how much computational power is required for an organism to generate sophisticated behaviors. Behaviors are typically associated with a nervous system, but many organisms without nervous systems integrate information and function as coordinated individuals (1); examples range from the ability of Escherichia coli to move up chemical gradients (2) to the ability of a multicellular fungus to sense and precisely explore unoccupied space (3). A recently published and striking example of a complex behavior involves bacteria within a biofilm: When a Bacillus subtilis biofilm is deprived of nutrients, bacteria are able to grow networks of channels and evaporatively pump flows, creating intricate structures that benefit the entire community (4).Perhaps the archetypal example of an apparently simple organism able to generate sophisticated behaviors is the slime mold Physarum polycephalum, whose behaviors are repeatedly characterized as "intelligent." This slime mold is able to navigate mazes by finding the shortest route between different food sources (5) and has used its ability to reconstruct the transportation maps of major cities (6). The organism can structure its connections to different nutrient sources to optimize its diet...

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Section: Discussionmentioning

confidence: 99%

Section: Conservation Of Mass Allows Calculation Of Mean Concentrationmentioning

confidence: 99%

Mechanism of signal propagation in Physarum polycephalum

Alim

Andrew

Pringle

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…In passive systems the particles either do not have any intelligence at all (but just move and bond based on their structural properties or due to chemical interactions with the environment), or they have limited computational capabilities but cannot control their movements. Examples of research on passive systems are DNA computing [1,9,15,21,39], tile self-assembly systems in general (e.g., see the surveys in [23,35,40]), population protocols [4], and slime molds [10,33]. We will not describe these models in detail as they are only of little relevance for our approach.…”

Section: Related Workmentioning

confidence: 99%

Leader Election and Shape Formation with Self-organizing Programmable Matter

Derakhshandeh

Gmyr

Strothmann

et al. 2015

Lecture Notes in Computer Science

120

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Abstract. We consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a suitable model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in SPAA 2014. Based on the geometric model, we present efficient localcontrol algorithms for leader election and line formation requiring only particles with constant size memory, and we also discuss the limitations of solving these problems within the general amoebot model.

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“…In passive systems, particles move based only on their structural properties and interactions with their environment, or have only limited computational ability but lack control of their motion. Examples include population protocols [5] as well as molecular computing models such as DNA self-assembly systems (see, e.g., the surveys in [6,7,8]) and slime molds [9,10].…”

Section: Related Workmentioning

confidence: 99%

On the Runtime of Universal Coating for Programmable Matter

Derakhshandeh

Gmyr

Porter

et al. 2016

Lecture Notes in Computer Science

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Abstract. Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our Universal Coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice.

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Physarum Can Compute Shortest Paths

Cited by 56 publications

References 4 publications

Mechanism of signal propagation in Physarum polycephalum

Mechanism of signal propagation in Physarum polycephalum

Leader Election and Shape Formation with Self-organizing Programmable Matter

On the Runtime of Universal Coating for Programmable Matter

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