Cellular automata modelling of slime mould actin network signalling

Mayne, R. and Adamatzky, A. (2016) Cellular automata modelling of slime mould actin network signalling. Natural Computing. ISSN 1567-7818 Available from: http://eprints.uwe.ac.uk/28903

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Publisher's URL: http://dx.doi.org/10.1007/s11047-016-9559-0

Abstract/Description

Actin is a cytoskeletal protein which forms dense, highly interconnected networks within eukaryotic cells. A growing body of evidence suggests that actin- mediated intra- and extracellular signalling is instrumental in facilitating organism-level emergent behaviour patterns which, crucially, may be characterised as natural expres- sions of computation. We use excitable cellular automata modelling to simulate signal transmission through cell arrays whose topology was extracted from images of Watershed transformation-derived actin network recon- structions; the actin networks sampled were from labora- tory experimental observations of a model organism, slime mould Physarum polycephalum. Our results indicate that actin networks support directional transmission of gener- alised energetic phenomena, the amplification and trans- network speed of which of which is proportional to net- work density (whose primary determinant is the anatomical location of the network sampled). Furthermore, this model also suggests the ability of such networks for supporting signal-signal interactions which may be characterised as Boolean logical operations, thus indicating that a cell’s actin network may function as a nanoscale data transmis- sion and processing network. We conclude by discussing the role of the cytoskeleton in facilitating intracellular computing, how computation can be implemented in such a network and practical considerations for designing ‘useful’ actin circuitry.

Item Type:Article
Uncontrolled Keywords:physarum polycephalum, watershed transformation, actin, cytoskeleton, cellular automata, unconventional computing
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:28903
Deposited By: Dr R. Mayne
Deposited On:23 May 2016 08:19
Last Modified:10 Jul 2017 02:53

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