Auto-oscillatory Processes and Feedback Mechanisms in Physarum Plasmodium Motility

Teplov, V. A.; Romanovsky, Yuri M.; Павлов, Д. А.; Alt, Wolfgang

doi:10.1007/978-3-0348-8916-2_10

Cited by 10 publications

(4 citation statements)

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“…Signals may propagate via elastic waves (14) or an advected molecular stimulus (15,16) or electrical impulses (17). Our very recent work tentatively suggests the second hypothesis; flows generated from the coordinated contractions of tube walls are used to increase the effective dispersion of molecules substantially beyond their pure molecular diffusivity, a phenomenon known as Taylor dispersion (18).…”

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confidence: 89%

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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confidence: 89%

Mechanism of signal propagation in Physarum polycephalum

Alim

Andrew

Pringle

et al. 2017

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

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“…Pattern formation of the rhythmic contractions is involved in the coordination of local contractile activity and has been considered from two viewpoints: first, the theory of so-called coupled oscillator systems (22)(23)(24)(25)(26)(27)(28); and second, the theory of complex viscoelastic matter (29)(30)(31)(32)(33). To unify these two views, it is essential to clarify how the movement of the plasmodium is generated by the spatiotemporal pattern of contraction and streaming.…”

Section: Introductionmentioning

confidence: 99%

Locomotive Mechanism of Physarum Plasmodia Based on Spatiotemporal Analysis of Protoplasmic Streaming

Matsumoto

Takagi

Nakagaki

2008

Biophysical Journal

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We investigate how an amoeba mechanically moves its own center of gravity using the model organism Physarum plasmodium. Time-dependent velocity fields of protoplasmic streaming over the whole plasmodia were measured with a particle image velocimetry program developed for this work. Combining these data with measurements of the simultaneous movements of the plasmodia revealed a simple physical mechanism of locomotion. The shuttle streaming of the protoplasm was not truly symmetric due to the peristalsis-like movements of the plasmodium. This asymmetry meant that the transport capacity of the stream was not equal in both directions, and a net forward displacement of the center of gravity resulted. The generality of this as a mechanism for amoeboid locomotion is discussed.

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“…In the project Physarum chip project: growing computers from slime mould, supported by FP7 and organized by Andrew Adamatzky, we have designed some processors on the basis of P. polycephalum motions. There are some other models to formalize the plasmodium behaviour: a memory model [58,80], an auto-oscillatory model [77,78] and many others. The memory model uses memory circuit elements, such as resistors, capacitors and inductors, with memory whose state depends on the history of signals applied [58,80].…”

Section: Introductionmentioning

confidence: 99%

“…The memory model uses memory circuit elements, such as resistors, capacitors and inductors, with memory whose state depends on the history of signals applied [58,80]. In the auto-oscillatory model, auto-oscillations and autowaves are explained on the basis of properties of the actomyosin filament system [78]. The plasmodium of P. polycephalum consists of various proteins, among which actin filament networks responsible for the plasmodial cytoskeleton participate in the intelligent behaviour of P. polycephalum [50].…”

Section: Introductionmentioning

confidence: 99%

Decidable and undecidable arithmetic functions in actin filament networks

Schumann¹

2017

J. Phys. D: Appl. Phys.

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The plasmodium of Physarum polycephalum, called slime mould, is very sensitive to its environment and reacts to stimuli by its appropriate motions. The sensitive stage as well as the motor stage of these reactions are explained by actin filament networks. This paper is devoted to actin filament networks as a computation medium. The point is that actin filaments are sensitive to outer cellular stimuli (attractants as well as repellents) and they appear and disappear at different places of the cell to change the cell structure, e.g. its shape. Due to assembling and disassembling actin filaments, Physarum polycephalum as well as other unicellular organisms like Amoeba proteus can move in responses to different stimuli. As a result, these organisms can be considered a simple reversible logic gate, where outer cellular signals are its inputs and the motions are its outputs. In this way, we can implement different logic gate on the amoeboid behaviours. The actin filament networks have the same basic properties as neural networks: lateral inhibition; lateral activation; recurrent inhibition; recurrent excitation; feedforward inhibition; feedforward excitation; convergence/divergence. These networks can embody arithmetic functions defined recursively and corecursively within p-adic valued logic. Furthermore, within these networks we can define the so-called diagonalization for deducing undecidable arithmetic functions.

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Auto-oscillatory Processes and Feedback Mechanisms in Physarum Plasmodium Motility

Cited by 10 publications

References 0 publications

Mechanism of signal propagation in Physarum polycephalum

Mechanism of signal propagation in Physarum polycephalum

Locomotive Mechanism of Physarum Plasmodia Based on Spatiotemporal Analysis of Protoplasmic Streaming

Decidable and undecidable arithmetic functions in actin filament networks

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