Skip to main content
Springer Nature Link
Log in

Chemical Computing

  • Conference paper
Unconventional Programming Paradigms (UPP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3566))

Included in the following conference series:

Abstract

All information processing systems found in living organisms are based on chemical processes. Harnessing the power of chemistry for computing might lead to a new unifying paradigm coping with the rapidly increasing complexity and autonomy of computational systems. Chemical computing refers to computing with real molecules as well as to programming electronic devices using principles taken from chemistry. The paper focuses on the latter, called artificial chemical computing, and discusses several aspects of how the metaphor of chemistry can be employed to build technical information processing systems. In these systems, computation emerges out of an interplay of many decentralized relatively simple components analogized to molecules. Chemical programming encompassed then the definition of molecules, reaction rules, and the topology and dynamics of the reaction space. Due to the self-organizing nature of chemical dynamics, new programming methods are required. Potential approaches for chemical programming are discussed and a road map for developing chemical computing into a unifying and well grounded approach is sketched.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Banâtre, J.P., Métayer, D.L.: The GAMMA model and its discipline of programming. Sci. Comput. Program. 15, 55–77 (1990)

    Article  MATH  Google Scholar 

  2. Berry, G., Boudol, G.: The chemical abstract machine. Theor. Comput. Sci. 96, 217–248 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  3. Adamatzky, A.: Universal dynamical computation in multidimensional excitable lattices. Int. J. Theor. Phys. 37, 3069–3108 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  4. Dörner, D.: Bauplan einer Seele. Rowohlt, Reinbeck (1999)

    Google Scholar 

  5. Brooks, R.A.: Coherent behavior from many adaptive processes. In: Cliff, D., Husbands, P., Meyer, J.A., Wilson, S. (eds.) From animals to animats, vol. 3, pp. 22–29. MIT Press, Cambridge (1994)

    Google Scholar 

  6. Conrad, M.: Information processing in molecular systems. Currents in Modern Biology 5, 1–14 (1972)

    Google Scholar 

  7. Liberman, E.A.: Cell as a molecular computer (MCC). Biofizika 17, 932–943 (1972)

    Google Scholar 

  8. Liberman, E.A.: Analog-digital molecular cell. BioSytems 11, 111–124 (1979)

    Article  MathSciNet  Google Scholar 

  9. Zauner, K.P.: Molecular information technology. Cr. Rev. Sol. State 30, 33–69 (2005)

    Article  Google Scholar 

  10. Tilden, W.A.: Introduction to the Study of Chemical Philosophy, 6th edn. Longmans, London (1888)

    Google Scholar 

  11. Küppers, B.O.: Information and the Origin of Life. MIT Press, Cambridge (1990)

    Google Scholar 

  12. Adleman, L.M.: Molecular computation of solutions to combinatorical problems. Science 266, 1021 (1994)

    Article  Google Scholar 

  13. Conrad, M., Zauner, K.P.: Conformation-driven computing: A comparison of designs based on DNA, RNA, and protein. Supramol. Sci. 5, 787–790 (1998)

    Article  Google Scholar 

  14. Hjelmfelt, A., Weinberger, E.D., Ross, J.: Chemical implementation of neural networks and turing machines. In: Proc. Natl. Acad. Sci., USA, vol. 88, pp. 10983–10987 (1991)

    Google Scholar 

  15. Bazhenov, V.Y., Soskin, M.S., Taranenko, V.B., Vasnetsov, M.V.: Biopolymers for real-time optical processing. In: Arsenault, H.H. (ed.) Optical Processing and Computing, San Diego, pp. 103–144. Academic Press, London (1989)

    Google Scholar 

  16. Pǎun, G.: Computing with membranes. J. Comput. Syst. Sci. 61, 108–143 (2000)

    Article  Google Scholar 

  17. Giavitto, J.L., Michel, O.: MGS: a rule-based programming language for complex objects and collections. In: van den Brand, M., Verma, R. (eds.) Electronic Notes in Theoretical Computer Science, vol. 59. Elsevier Science Publishers, Amsterdam (2001)

    Google Scholar 

  18. Laing, R.: Artificial organisms and autonomous cell rules. J. Cybernetics 2, 38–49 (1972)

    Article  Google Scholar 

  19. Suzuki, H., Ono, N., Yuta, K.: Several necessary conditions for the evolution of complex forms of life in an artificial environment. Artif. Life 9, 153–174 (2003)

    Article  Google Scholar 

  20. Banâtre, J.P., Métayer, D.L.: A new computational model and its discipline of programming. Technical report RR-0566, INRIA (1986)

    Google Scholar 

  21. Suzuki, Y., Tanaka, H.: Symbolic chemical system based on abstract rewriting and its behavior pattern. Artif. Life and Robotics 1, 211–219 (1997)

    Article  Google Scholar 

  22. Suzuki, Y., Tsumoto, S., Tanaka, H.: Analysis of cycles in symbolic chemical system based on abstract rewriting system on multisets. In: Langton, C.G., Shimohara, K. (eds.) Artificial Life V, Cambridge, MA, pp. 521–528. MIT Press, Cambridge (1996)

    Google Scholar 

  23. Cliff, D., Grand, S.: The creatures global digital ecosystem. Artif. Life 5, 77–94 (1999)

    Article  Google Scholar 

  24. Astor, J.C., Adami, C.: A developmental model for the evolution of artificial neural networks. Artif. Life 6, 189–218 (2000)

    Article  Google Scholar 

  25. Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T.F., Nagpal, R., Rauch, E., Sussman, G.J., Weiss, R., Homsy, G.: Amorphous computing. Commun. ACM 43, 74–82 (2000)

    Article  Google Scholar 

  26. Husbands, P., Smith, T., Jakobi, N., O’Shea, M.: Better living through chemistry: Evolving gasnets for robot control. Connect. Sci. 10, 185–210 (1998)

    Article  Google Scholar 

  27. Seelig, L.A., Rössler, O.E.: A chemical reaction flip-flop with one unique switching input. Zeitschrift für Naturforschung 27b, 1441–1444 (1972)

    Google Scholar 

  28. Barrett, C.L., Mortveit, H.S., Reidys, C.M.: Elements of a theory of simulation II: sequential dynamical systems. Appl. Math. Comput. 107, 121–136 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  29. Reidys, C.M.: On acyclic orientations and sequential dynamical systems. Adv. Appl. Math. 27, 790–804 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  30. Dittrich, P., di Fenizio, P.S.: Chemical organization theory: Towards a theory of constructive dynamical systems , preprint arXiv:q-bio.MN/0501016 x, pp. 1–7 (2005) (submitted)

    Google Scholar 

  31. Ziegler, J., Banzhaf, W.: Evolving control metabolisms for a robot. Artif. Life 7, 171–190 (2001)

    Article  Google Scholar 

  32. Bedau, M.A., Buchanan, A., Gazzola, G., Hanczyc, M., Maeke, T., McCaskill, J., Poli, I., Packard, N.H.: Evolutionary design of a DDPD model of ligation. In: Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871, pp. 201–212. Springer, Heidelberg (2006) (in press)

    Chapter  Google Scholar 

  33. Speroni Di Fenizio, P., Dittrich, P.: Artificial chemistry’s global dynamics. movement in the lattice of organisation. The Journal of Three Dimensional Images 16, 160–163 (2002)

    Google Scholar 

  34. Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial chemistries - a review. Artif. Life 7, 225–275 (2001)

    Article  Google Scholar 

  35. Banzhaf, W.: Self-replicating sequences of binary numbers – foundations I and II: General and strings of length n = 4. Biol. Cybern. 69, 269–281 (1993)

    Article  MATH  Google Scholar 

  36. Fontana, W., Buss, L.W.: ’The arrival of the fittest’: Toward a theory of biological organization. Bull. Math. Biol. 56, 1–64 (1994)

    MATH  Google Scholar 

  37. Benkö, G., Flamm, C., Stadler, P.F.: A graph-based toy model of chemistry. J. Chem. Inf. Comput. Sci. 43, 2759–2767 (2003)

    Google Scholar 

  38. Gillespie, D.T.: Exact stochastic simulation of coupled chemical-reactions. J. Phys. Chem. 81, 2340–2361 (1977)

    Article  Google Scholar 

  39. Mayer, B., Rasmussen, S.: Dynamics and simulation of micellar self-reproduction. Int. J. Mod. Phys. C 11, 809–826 (2000)

    Google Scholar 

  40. Cardelli, L.: Brane calculi. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 257–278. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  41. Dittrich, P.: Selbstorganisation in einem System von Binärstrings mit algorithmischen Sekundärstrukturen. Diploma thesis, Dept. of Computer Science, University of Dortmund (1995)

    Google Scholar 

  42. Speroni di Fenizio, P.: A less abstract artficial chemistry. In: Bedau, M.A., McCaskill, J.S., Packard, N.H., Rasmussen, S. (eds.) Artificial Life VII, pp. 49–53. MIT Press, Cambridge (2000)

    Google Scholar 

  43. Banzhaf, W., Dittrich, P., Rauhe, H.: Emergent computation by catalytic reactions. Nanotechnology 7, 307–314 (1996)

    Article  Google Scholar 

  44. Eigen, M., Schuster, P.: The hypercycle: a principle of natural self-organisation, part A. Naturwissenschaften 64, 541–565 (1977)

    Article  Google Scholar 

  45. Fontana, W., Wagner, G., Buss, L.W.: Beyond digital naturalism. Artif. Life 1(2), 211–227 (1994)

    Article  Google Scholar 

  46. Dittrich, P., Banzhaf, W.: Self-evolution in a constructive binary string system. Artif. Life 4, 203–220 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB) and Department of Mathematics and Computer Science, Friedrich-Schiller-University Jena, D-07743, Jena, Germany

    Peter Dittrich

Authors
  1. Peter Dittrich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Université de Rennes I and INRIA/IRISA, Campus de Beaulieu, 35042, Rennes Cedex, France

    Jean-Pierre Banâtre

  2. INRIA Rhône-Alpes - POP ART project, 655 avenue de l’Europe, 38330, Montbonnot, France

    Pascal Fradet

  3. LaMI UMR 8042 CNRS – Université d’Evry, Genopole, 523 place des Terrasses de l’Agora, 91000, Evry, France

    Jean-Louis Giavitto  & Olivier Michel  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dittrich, P. (2005). Chemical Computing. In: Banâtre, JP., Fradet, P., Giavitto, JL., Michel, O. (eds) Unconventional Programming Paradigms. UPP 2004. Lecture Notes in Computer Science, vol 3566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527800_2

Download citation

  • DOI: https://doi.org/10.1007/11527800_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27884-9

  • Online ISBN: 978-3-540-31482-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics