Computational Ability of Cells based on Cell Dynamics and Adaptability

Nakagaki, Toshiyuki; Tero, Atsushi; Kobayashi, Ryo; Onishi, Isamu; Miyaji, Tomoyuki

doi:10.1007/s00354-008-0054-8

Cited by 30 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The magnitude of Q 0 plays an important role in the various interactions between the elementary components of these types of models (veins in this case), and is particularly influential on the number of tubes that persist in the simulation. Thus the final network architecture produced by the model is highly dependent on Q 0 and the form of f , as discussed previously [6,11,17]. Fig.…”

Section: Path-finding and Multi-functionally Networking Derived By Momentioning

confidence: 61%

Adaptive Path-Finding and Transport Network Formation by the Amoeba-Like Organism Physarum

Kunita

Yoshihara

Tero

et al. 2013

Proceedings in Information and Communications Technology

Self Cite

View full text Add to dashboard Cite

Abstract. The giant amoeba-like plasmodia of Physarum is able to solve the shortest path through a maze and construct near optimal functional networks between multiple, spatially distributed food-sources. These phenomena are interesting as they provide clues to potential biological computational algorithms that operate in a de-centralized, singlecelled system. We report here some factors that can affect path-finding through networks. These findings help us to understand more generally how the organism tries to establish an optimal set of paths in more complex environments and how this behaviour can be captured in relatively simple algorithms.

show abstract

Section: Path-finding and Multi-functionally Networking Derived By Momentioning

confidence: 61%

Adaptive Path-Finding and Transport Network Formation by the Amoeba-Like Organism Physarum

Kunita

Yoshihara

Tero

et al. 2013

Proceedings in Information and Communications Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…A number of other more general computational abilities related to the dynamics of the network have been reported [14][15][16]. Mathematical models have been developed which demonstrate how adaptable structures can be used to process information [17,18], but these models were phenomenological; they do not address the mechanism of how the tubes reorganize.…”

Section: Fig 1 (Color Online) Macroscopic Images Ofmentioning

confidence: 99%

Flow-induced channel formation in the cytoplasm of motile cells

2011

Self Cite

View full text Add to dashboard Cite

A model is presented to explain the development of flow channels within the cytoplasm of the plasmodium of the giant amoeba Physarum polycephalum. The formation of channels is related to the development of a self-organizing tubular network in large cells. Experiments indicate that the flow of cytoplasm is involved in the development and organization of these networks, and the mathematical model proposed here is motivated by recent experiments involving the observation of development of flow channel in small cells. A model of pressure-driven flow through a polymer network is presented in which the rate of flow increases the rate of depolymerization. Numerical solutions and asymptotic analysis of the model in one spatial dimension show that under very general assumptions this model predicts the formation of channels in response to flow.

show abstract

“…Thus, theoretical research guiding the design of meaningful (and realistically feasible) experiments becomes very important. Here we present a theoretical study that analyzes fundamental properties of dynamic decision making by the true slime mold Physarum polycephalum , an important model species for the study of information processing in biological systems [6, 7]. Our study directly suggests foraging experiments that will allow us to verify these properties for the real system.…”

Section: Introductionmentioning

confidence: 99%

“…About a decade ago, a seminal experiment [4] demonstrated that P. polycephalum can solve the shortest path problem in mazes. Since then, these studies have been extended and it has been demonstrated that it can solve (or approximately solve) a variety of other network optimization problems [7, 17, 18] even when taking multiple objectives into account [19]. It has also been shown that P. polycephalum possesses a memory and is able to anticipate periodic events [20].…”

Section: Introductionmentioning

confidence: 99%

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

2017

Self Cite

View full text Add to dashboard Cite

Self-organized mechanisms are frequently encountered in nature and known to achieve flexible, adaptive control and decision-making. Noise plays a crucial role in such systems: It can enable a self-organized system to reliably adapt to short-term changes in the environment while maintaining a generally stable behavior. This is fundamental in biological systems because they must strike a delicate balance between stable and flexible behavior. In the present paper we analyse the role of noise in the decision-making of the true slime mold Physarum polycephalum, an important model species for the investigation of computational abilities in simple organisms. We propose a simple biological experiment to investigate the reaction of P. polycephalum to time-variant risk factors and present a stochastic extension of an established mathematical model for P. polycephalum to analyze this experiment. It predicts that—due to the mechanism of stochastic resonance—noise can enable P. polycephalum to correctly assess time-variant risk factors, while the corresponding noise-free system fails to do so. Beyond the study of P. polycephalum we demonstrate that the influence of noise on self-organized decision-making is not tied to a specific organism. Rather it is a general property of the underlying process dynamics, which appears to be universal across a wide range of systems. Our study thus provides further evidence that stochastic resonance is a fundamental component of the decision-making in self-organized macroscopic and microscopic groups and organisms.

show abstract

Computational Ability of Cells based on Cell Dynamics and Adaptability

Cited by 30 publications

References 25 publications

Adaptive Path-Finding and Transport Network Formation by the Amoeba-Like Organism Physarum

Adaptive Path-Finding and Transport Network Formation by the Amoeba-Like Organism Physarum

Flow-induced channel formation in the cytoplasm of motile cells

The role of noise in self-organized decision making by the true slime mold Physarum polycephalum

Contact Info

Product

Resources

About