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Chemical computing with reaction-diffusion processes

Profile image of J. GuzowskiJ. Guzowski

2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

https://doi.org/10.1098/RSTA.2014.0219
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Abstract

One contribution of 13 to a Theo Murphy meeting issue 'Heterotic computing: exploiting hybrid computational devices' .

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References (60)

  1. Moore GE. 1965 Cramming more components onto integrated circuits. Electronics, pp. 114-117, 19 April. For recent data see for example http://en.wikipedia.org/wiki/Moore's_law.
  2. Gramss T, Bornholdt S, Gross M, Mitchell M, Pellizzari T. 1998 Non-standard computation: molecular computation, cellular automata, evolutionary algorithms, quantum computers. Weinheim, Germany: Wiley-VCH.
  3. Calude C, Paun G. 2000 Computing with cells and atoms: an introduction to quantum, DNA and membrane computing. Boca Raton, FL: CRC Press.
  4. Adamatzky A, De Lacy Costello B, Asai T. 2005 Reaction-diffusion computers. New York, NY: Elsevier.
  5. Adamatzky A, De Lacy Costello B, Bull L, Stepney S, Teuscher C (eds). 2007 Unconventional computing. Bristol, UK: Luniver.
  6. Suzuki Y, Hagiya M, Umeo H, Adamatzky A (eds). 2009 Natural computing. Tokyo, Japan: Springer.
  7. Feynman RP. 1996 The Feynman lectures on computation (eds AJG Hey, RW Allen), ch. 1. Reading, MA: Addison-Wesley.
  8. Haken H. 2002 Brain dynamics, synchronization and activity patterns in pulse-coupled neural nets with delays and noise. Berlin, Germany: Springer.
  9. Epstein IR, Pojman JA. 1998 An introduction to nonlinear chemical dynamics: oscillations, waves, patterns, and chaos. New York, NY: Oxford University Press.
  10. Field RJ, Burger M. 1985 Oscillations and traveling waves in chemical systems. New York, NY: Wiley.
  11. Tyson JJ. 1994 What everyone should know about the Belousov-Zhabotinsky reaction. In Frontiers in mathematical biology (ed. SA Levin), pp. 569-587. Berlin, Germany: Springer.
  12. Kuhnert L. 1986 A new optical photochemical memory device in a light-sensitive chemical active medium. Nature 319, 393-394. (doi:10.1038/319393a0)
  13. Kuhnert L, Agladze KI, Krinsky VI. 1989 Image processing using light-sensitive chemical waves. Nature 337, 244-247. (doi:10.1038/337244a0)
  14. Rambidi NG, Maximychev AV. 1997 Towards a biomolecular computer. Information processing capabilities of biomolecular nonlinear dynamic media. BioSystems 41, 195-211. (doi:10.1016/S0303-2647(96)01678-4)
  15. Steinbock O, Toth A, Showalter K. 1995 Navigating complex labyrinths-optimal paths from chemical waves. Science 267, 868-871. (doi:10.1126/science.267.5199.868)
  16. Agladze K, Magome N, Aliev R, Yamaguchi T, Yoshikawa K. 1997 Finding the optimal path with the aid of chemical wave. Phys. D 106, 247-254. (doi:10.1016/S0167-2789(97)00049-3)
  17. Vazquez-Otero A, Faigl J, Munuzuri AP. 2011 Path planning based on reaction-diffusion process. See http://www.usc.es/export/sites/default/en/investigacion/grupos/gfnl/ documents/papers/Vazquez_Otero_2012.pdf. on August 13, 2015 http://rsta.royalsocietypublishing.org/ Downloaded from rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 373: 20140219 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  18. Gorecki J, Gorecka JN. 2009 Computing in geometrical constrained excitable chemical systems. In Encyclopedia of complexity and systems science (ed. RA Meyers), pp. 1352-1375. Berlin, Germany: Springer.
  19. Toth A, Showalter K. 1995 Logic gates in excitable media. J. Chem. Phys. 103, 2058-2066. (doi:10.1063/1.469732)
  20. Steinbock O, Toth A, Showalter K. 1995 Navigating complex labyrinths-optimal paths from chemical waves. Science 267, 868-871. (doi:10.1126/science.267.5199.868)
  21. Steinbock O, Kettunen P, Showalter K. 1996 Chemical wave logic gates. J. Phys. Chem. 100, 18970-18975. (doi:10.1021/jp961209v)
  22. Motoike IN, Yoshikawa K. 1999 Information operations with an excitable field. Phys. Rev. E 59, 5354-5360. (doi:10.1103/PhysRevE.59.5354)
  23. Sielewiesiuk J, Gorecki J. 2001 Logical functions of a cross junction of excitable chemical media. J. Phys. Chem. A 105, 8189-8195. (doi:10.1021/jp011072v)
  24. Adamatzky A, De Lacy Costello B. 2002 Experimental logical gates in a reaction- diffusion medium: the XOR gate and beyond. Phys. Rev. E 66, 046112. (doi:10.1103/Phys RevE.66.046112)
  25. Yoshikawa K, Motoike IN, Ichino T, Yamaguchi T, Igarashi Y, Gorecki J, Gorecka JN. 2009 Basic information processing operations with pulses of excitation in a reaction-diffusion system. Int. J. Unconv. Comput. 5, 3-37.
  26. Gorecki J, Gorecka JN. 2006 Information processing with chemical excitations-from instant machines to an artificial chemical brain. Int. J. Unconv. Comput. 2, 321-336.
  27. Vanag VK, Epstein IR. 2001 Pattern formation in a tunable medium: the Belousov- Zhabotinsky reaction in an aerosol OT microemulsion. Phys. Rev. Lett. 87, 228301. (doi:10. 1103/PhysRevLett.87.228301)
  28. Szymanski J, Gorecka JN, Igarashi Y, Gizynski K, Gorecki J, Zauner KP, de Planque M. 2011 Droplets with information processing ability. Int. J. Unconv. Comput. 7, 185-200.
  29. Field RJ, Koros E, Noyes RM. 1972 Oscillations in chemical systems. II. Thorough analysis of temporal oscillation in the bromate-cerium-malonic acid system. J. Am. Chem. Soc. 94, 8649- 8664. (doi:10.1021/ja00780a001)
  30. Field RJ, Noyes RM. 1974 Oscillations in chemical systems. IV. Limit cycle behavior in a model of a real chemical reaction. J. Chem. Phys. 60, 1877-1884. (doi:10.1063/1.1681288)
  31. Vanag VK, Epstein IR. 2009 A model for jumping and bubble waves in the Belousov- Zhabotinsky-aerosol OT system. J. Chem. Phys. 131, 104512. (doi:10.1063/1.3231488)
  32. Rovinsky AB, Zhabotinsky AM, Irving R. 1984 Mechanism and mathematical model of the oscillating bromate-ferroin-bromomalonic acid reaction. J. Phys. Chem. 88, 6081-6084. (doi:10.1021/j150669a001)
  33. Gorecki J, Szymanski J, Gorecka JN. 2011 Realistic parameters for simple models of the Belousov-Zhabotinsky reaction. J. Phys. Chem. A 115, 8855-8859. (doi:10.1021/jp203220g)
  34. Adamatzky A, De Lacy Costello B, Bull L, Holley J. 2011 Towards arithmetic circuits in sub- excitable chemical media. Isr. J. Chem. 51, 56-66. (doi:10.1002/ijch.201000046)
  35. Suzuki K, Yoshinobu T, Iwasaki H. 2000 Unidirectional propagation of chemical waves through microgaps between zones with different excitability. J. Phys. Chem. A 104, 6602-6608. (doi:10.1021/jp0007078)
  36. Sielewiesiuk J, Gorecki J. 2002 Passive barrier as a transformer of 'chemical signal' frequency. J. Phys. Chem. A 106, 4068-4076. (doi:10.1021/jp013844t)
  37. Sielewiesiuk J, Gorecki J. 2002 On the response of simple reactors to regular trains of pulses. Phys. Chem. Chem. Phys. 4, 1326-1333. (doi:10.1039/b109522f)
  38. Gorecki J, Gorecka JN, Yoshikawa K, Igarashi Y, Nagahara H. 2005 Sensing the distance to a source of periodic oscillations in a nonlinear chemical medium with the output information coded in frequency of excitation pulses. Phys. Rev. E 72, 046201. (doi:10.1103/ PhysRevE.72.046201)
  39. Gorecki J, Yoshikawa K, Igarashi Y. 2003 On chemical reactors that can count. J. Phys. Chem. A 107, 1664-1669. (doi:10.1021/jp0210417)
  40. Gorecka JN, Gorecki J. 2006 Multiargument logical operations performed with excitable chemical medium. J. Chem. Phys. 124, 084101. (doi:10.1063/1.2170076)
  41. Turing A. 1936 On computable numbers, with an application to Entscheidungsproblem. Proc. Lond. Math. Soc. 42, 230-265.
  42. Adamatzky A. 2004 Collision-based computing in Belousov-Zhabotinsky medium. Chaos Solitons Fractals 21, 1259-1264. (doi:10.1016/j.chaos.2003.12.068) on August 13, 2015 http://rsta.royalsocietypublishing.org/ Downloaded from rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 373: 20140219 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  43. Adamatzky A, De Lacy Costello B, Bull L. 2011 On polymorphic logical gates in subexcitable chemical medium. Int. J. Bifurcation Chaos 21, 1977-1986. (doi:10.1142/S0218127411029574)
  44. Adamatzky A, Holley J, Bull L, De Lacy Costello B. 2011 On computing in fine-grained compartmentalised Belousov-Zhabotinsky medium. Chaos Solitons Fractals 44, 779-790. (doi:10.1016/j.chaos.2011.03.010)
  45. Holley J, Adamatzky A, Bull L, De Lacy Costello B, Jahan I. 2011 Computational modalities of Belousov-Zhabotinsky encapsulated vesicles. Nano Commun. Netw. 2, 50-61. (doi:10.1016/j.nancom.2011.02.002)
  46. Holley J, Jahan I, De Lacy Costello B, Bull L, Adamatzky A. 2011 Logical and arithmetic circuits in Belousov-Zhabotinsky encapsulated disks. Phys. Rev. E 84, 056110. (doi:10.1103/ PhysRevE.84.056110)
  47. NeuNeu Project. Artificial wet neuronal networks from compartmentalised excitable chemical media. See http://neu-n.eu.
  48. Vanag VK, Epstein IR. 2003 Segmented spiral waves in a reaction-diffusion system. Proc. Natl Acad. Sci. USA 100, 14 635-14 638. (doi:10.1073/pnas.2534816100)
  49. Toiya M, Vanag VK, Epstein IR. 2008 Diffusively coupled chemical oscillators in a microfluidic assembly. Angew. Chem. 47, 7753-7755. (doi:10.1002/anie.200802339)
  50. Toiya M, Gonzalez-Ochoa HO, Vanag VK, Fraden S, Epstein IR. 2010 Synchronization of chemical micro-oscillators. J. Phys. Chem. Lett. 1, 1241-1246. (doi:10.1021/jz100238u)
  51. Thutupalli S, Herminghaus S. 2013 Tuning active emulsion dynamics via surfactants and topology. Eur. Phys. J. E 36, 91. (doi:10.1140/epje/i2013-13091-2)
  52. Vanag VK, Epstein IR. 2011 Excitatory and inhibitory coupling in a one-dimensional array of Belousov-Zhabotinsky micro-oscillators: theory. Phys. Rev. E 84, 066209. (doi:10.1103/ PhysRevE.84.066209)
  53. Churski K, Korczyk PM, Garstecki P. 2010 High-throughput automated droplet microfluidic system for screening of reaction conditions. Lab Chip 10, 816-818. (doi:10.1039/b925500a)
  54. Guzowski J, Jakiela S, Korczyk PM, Garstecki P. 2013 Custom tailoring multiple droplets one-by-one. Lab Chip 13, 4308-4311. (doi:10.1039/c3lc50841b)
  55. Holden MA, Needham D, Bayley H. 2007. Functional bionetworks from nanoliter water droplets. J. Am. Chem. Soc. 129, 8650-8655. (doi:10.1021/ja072292a)
  56. Gorecki J, Gorecka JN, Adamatzky A. 2014 Information coding with frequency of oscillations in Belousov-Zhabotinsky encapsulated disks. Phys. Rev. E 89, 042910. (doi:10.1103/Phys RevE.89.042910)
  57. Steinbock O, Müller SC. 1998. Radius-dependent inhibition and activation of chemical oscillations in small droplets. J. Phys. Chem. A 102, 6485-6490. (doi:10.1021/jp981421u)
  58. Thurber D. 1992 Catfish are off the hook after Tokyo ends 16-year earthquake prediction study. Los Angeles Times, 26 April 1992. See http://articles.latimes.com/1992-04-26/ news/mn-1403_1_major-earthquake.
  59. Bache K, Lichman M. 2013 UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences.
  60. Gorecka JN, Gorecki J. 2005 On one dimensional chemical diode and frequency generator constructed with an excitable surface reaction. Phys. Chem. Chem. Phys. 7, 2915-2920. (doi:10.10 39/b504621a)

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