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Top1. Introduction
Developing physical, chemical and biological analogies of socio-economic processes are becoming increasingly popular nowadays because they give rise to new metaphors and uncover unique similarities. Successful examples of such cross-disciplinary fertilisation include the theory of fractal cities (Batty & Longley, 1994), leaf-inspired simulation of street network growth (Runions, Fuhrer, Lane, Federl, Rolland-Lagan, & Prusinkiewicz, 2005; Barthelemy & Flammini, 2008), urban theories by Alexander (1964) and Salingaros (2005), approaches relating urban morphology to biological morphogenesis (Mouson, 1997), and indeed the whole branch of socio-physics (Galam, 2012).
Despite the overwhelming success of the bio-inspired simulation and socio-physics, the prevailing majority of publications deal with purely theoretical works and computer simulations. Almost no attempts have been made to undertake experimental laboratory comparisons between very large-scale socio-economic developments and spatio-temporal dynamics of chemical or biological systems. This could be explained by difficulties in finding a suitable experimental substrate which does not require sophisticated laboratory equipment and expensive support. A breakthrough came in 2009 when first experimental results on imitating roads networks in United Kingdom with plasmodium of slime mould Physarum polycephalum were published (Adamatzky & Jones, 2010) followed by imitation of rail networks in Japan (Tero et al., 2010).
Plasmodium is a vegetative stage of acellular slime mould Physarum polycephalum. This is a single cell with many nuclei. The plasmodium fees on microscopic particles (Stephenson & Stempen, 2000). During its foraging behaviour the plasmodium spans scattered sources of nutrients with a network of protoplasmic tubes. The protoplasmic network is optimised to cover all sources of food and to provide a robust and speedy transportation of nutrients and metabolites in the plasmodium body. The plasmodium’s foraging behaviour can be interpreted as computation. Data are represented by spatial configurations of attractants and repellents, and results of computation by structures of protoplasmic network formed by the plasmodium on the data sites (Nakagaki, Yamada, & Ueda, 2000; Nakagaki, Yamada, & Toth, 2001; Adamatzky, 2010a). The problems solved by plasmodium of P. polycephalum include shortest path (Nakagaki, Yamada, & Ueda, 2000; Nakagaki, Yamada, & Toth, 2001), implementation of storage modification machines (Adamatzky, 2010a), Voronoi diagram (Shirakawa, Adamatzky, Gunji, & Miyake, 2009), Delaunay triangulation (Adamatzky, 2010a) logical computing (Tsuda, Aono, & Gunji, 2004; Adamatzky, 2010b), and process algebra (Schumann & Adamatzky, 2009; see overview in Adamatzky (2010a).