Bio-Development of Motorway Network in the Netherlands: a slime mould Approach

Plasmodium of a cellular slime mould Physarum polycephalum is a very large eukaryotic microbe visible to the unaided eye. During its foraging behavior the plasmodium spans sources of nutrients with a network of protoplasmic tubes. In this paper we attempt to address the following question: Is slime mould capable of computing transport networks? By assuming the sources of nutrients are cities and protoplasmic tubes connecting the sources are motorways, how well does the plasmodium approximate existing motorway networks? We take the Netherlands as a case study for bio-development of motorways, while it has the most dense motorway network in Europe, current demand is rapidly approaching the upper limits of existing capacity. We represent twenty major cities with oat flakes, place plasmodium in Amsterdam and record how the plasmodium spreads between oat flakes via the protoplasmic tubes. First we analyze slime-mould-built and man-built transport networks in a framework of proximity graphs to investigate if the slime mould is capable of computing existing networks. We then go on to investigate if the slime mould is able calculate or adapt the network through imitating restructuring of the transport network as a response to potential localized flooding of the Netherlands.

[1]  N. Trinajstic,et al.  On the Harary index for the characterization of chemical graphs , 1993 .

[2]  Andrew Adamatzky,et al.  Physarum machines: encapsulating reaction–diffusion to compute spanning tree , 2007, Naturwissenschaften.

[3]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[4]  M. Randic Characterization of molecular branching , 1975 .

[5]  T. Ueda,et al.  Interaction between cell shape and contraction pattern in the Physarum plasmodium. , 2000, Biophysical chemistry.

[6]  Andrew Adamatzky,et al.  From reaction-diffusion to Physarum computing , 2009, Natural Computing.

[7]  Godfried T. Toussaint,et al.  The relative neighbourhood graph of a finite planar set , 1980, Pattern Recognit..

[8]  Andrew Schumann,et al.  PHYSARUM SPATIAL LOGIC , 2011 .

[9]  D. Watanabe Evaluating the Configuration and the Travel Efficiency on Proximity Graphs as Transportation Networks , 2008 .

[10]  Andrew Adamatzky,et al.  Developing Proximity Graphs by Physarum polycephalum: Does the Plasmodium Follow the Toussaint Hierarchy? , 2009, Parallel Process. Lett..

[11]  S. Mahadevan,et al.  An amoeboid algorithm for solving linear transportation problem , 2014 .

[12]  Andrew Adamatzky,et al.  Hot ice computer , 2009, 0908.4426.

[13]  Andrew Adamatzky,et al.  Physarum Machine: Implementation of a Kolmogorov-Uspensky Machine on a Biological substrate , 2007, Parallel Process. Lett..

[14]  Pelin Alpkokin,et al.  Historical and critical review of spatial and transport planning in the Netherlands , 2012 .

[15]  D. Matula,et al.  Properties of Gabriel Graphs Relevant to Geographic Variation Research and the Clustering of Points in the Plane , 2010 .

[16]  T. Ueda,et al.  Modulation of cellular rhythm and photoavoidance by oscillatory irradiation in the Physarum plasmodium. , 1999, Biophysical chemistry.

[17]  Genaro Juárez Martínez,et al.  Approximating Mexican highways with slime mould , 2010, Natural Computing.

[18]  Andreas Manz,et al.  Glow discharge in microfluidic chips for visible analog computing. , 2002, Lab on a chip.

[19]  T. Nakagaki,et al.  Path finding by tube morphogenesis in an amoeboid organism. , 2001, Biophysical chemistry.

[20]  Andrew Adamatzky,et al.  Physarum Machines: Computers from Slime Mould , 2010 .

[21]  A. Adamatzky If BZ medium did spanning trees these would be the same trees as Physarum built , 2009 .

[22]  R. Sokal,et al.  A New Statistical Approach to Geographic Variation Analysis , 1969 .

[23]  Neil F Johnson,et al.  Interplay between function and structure in complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  D. Kirkpatrick,et al.  A Framework for Computational Morphology , 1985 .

[25]  Andrew Adamatzky,et al.  Slime mould logical gates: exploring ballistic approach , 2010, 1005.2301.

[26]  W. Relative Neighborhood Graphs and Their Relatives , 2004 .

[27]  S. Stephenson,et al.  Myxomycetes: A Handbook of Slime Molds , 1994 .

[28]  Jeff Jones,et al.  Road Planning with Slime Mould: if Physarum Built Motorways IT Would Route M6/M74 through Newcastle , 2009, Int. J. Bifurc. Chaos.

[29]  Tom Goemans,et al.  The delta project , 1987 .

[30]  Tetsuya Asai,et al.  Reaction-diffusion computers , 2005 .

[31]  T. Nakagaki,et al.  Smart behavior of true slime mold in a labyrinth. , 2001, Research in microbiology.

[32]  Anna Wesselink,et al.  Flood safety in the Netherlands: The Dutch response to Hurricane Katrina , 2007 .

[33]  Tomohiro Shirakawa,et al.  On Simultaneous Construction of Voronoi Diagram and Delaunay Triangulation by Physarum polycephalum , 2009, Int. J. Bifurc. Chaos.

[34]  Masashi Aono,et al.  Robust and emergent Physarum logical-computing. , 2004, Bio Systems.

[35]  Jaroslav Nesetril,et al.  Otakar Boruvka on minimum spanning tree problem Translation of both the 1926 papers, comments, history , 2001, Discret. Math..