Acta Informatica

, Volume 55, Issue 3, pp 227–267 | Cite as

Generalized contexts for reaction systems: definition and study of dynamic causalities

  • Roberto Barbuti
  • Roberta GoriEmail author
  • Francesca Levi
  • Paolo Milazzo
Original Article


Reaction systems are a qualitative formalism for the modelling of systems of biochemical reactions. In their original formulation, a reaction system executes in an environment (or context) that can supply it with new objects at each evolution step. The context drives the behaviour of a reaction system: it can provide different inputs to the system that can lead to different behaviours. In order to more faithfully deal with open systems, in this paper we propose a more powerful notion of context having not only the capability to provide objects, but also to absorb (or remove) objects at each evolution step. For such reaction systems with generalized context we investigate properties of dynamic causality by revising the previously proposed concept of formula based predictor. A formula based predictor is a Boolean formula characterising all contexts that lead to the production of a certain object after a given number of steps. In this paper, we revise the theory of formula based predictors in order to deal with reaction systems executed in a context of the new kind. As applications, we show an example of interaction between biochemical pathways and a reaction system modelling cell metabolism and respiration.


  1. 1.
    Barbuti, R., Gori, R., Levi, F., Milazzo, P.: Specialized predictor for reaction systems with context properties. In: Proceedings of the 24th International Workshop on Concurrency, Specification and Programming, CS&P 2015, pp. 31–43 (2015)Google Scholar
  2. 2.
    Barbuti, R., Gori, R., Levi, F., Milazzo, P.: Investigating dynamic causalities in reaction systems. Theor. Comput. Sci. 623, 114–145 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Barbuti, R., Gori, R., Levi, F., Milazzo, P.: Specialized predictor for reaction systems with context properties. Fundam. Inf. 147(2–3), 173–191 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bodei, C., Gori, R., Levi, F.: An analysis for causal properties of membrane interactions. Electr. Notes Theor. Comput. Sci. 299, 15–31 (2013)CrossRefzbMATHGoogle Scholar
  5. 5.
    Bodei, C., Gori, R., Levi, F.: Causal static analysis for Brane Calculi. Theor. Comput. Sci. 587, 73–103 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Brijder, R., Ehrenfeucht, A., Main, M.G., Rozenberg, G.: A tour of reaction systems. Int. J. Found. Comput. Sci. 22(7), 1499–1517 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Brijder, R., Ehrenfeucht, A., Rozenberg, G.: A note on causalities in reaction systems. Electronic Communications of the EASST 30 (2010). doi: 10.14279/tuj.eceasst.30.429
  8. 8.
    Brown, F.M.: Boolean Reasoning: The Logic of Boolean Equations. Kluwer, Amsterdam (1990)CrossRefzbMATHGoogle Scholar
  9. 9.
    De Castro, L.N.: Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. CRC Press, Boca Raton (2006)zbMATHGoogle Scholar
  10. 10.
    Dietmeyer, D.: Logic Design of Digital Systems. Allyn and Bacon, Boston (1978)zbMATHGoogle Scholar
  11. 11.
    Ehrenfeucht, A., Rozenberg, G.: Reaction systems. Fundam. Inf. 75(1–4), 263–280 (2007)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Fujita, M., Matsunaga, Y.: Multi-level logic minimization based on minimal support and its application to the minimization of look-up table type FPGAS. In: 1991 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 1993, Santa Clara, CA, USA, November 11–14, 1991. Digest of Technical Papers, pp. 560–563 (1991)Google Scholar
  13. 13.
    Gori, R., Levi, F.: Abstract interpretation based verification of temporal properties for bioambients. Inf. Comput. 208(8), 869–921 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Halatsis, C., Gaitanis, N.: Irredundant normal forms and minimal dependece sets of a Boolean function. IEEE Trans. Comput. 27(11), 1064–1068 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hassoun, S., Sasao, T. (eds.): Logic Synthesis and Verification. Kluwer, Amsterdam (2002)Google Scholar
  16. 16.
    Kambayashi, Y.: Logic design of programmable logic arrays. IEEE Trans. Comput. 28(9), 609–617 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kari, L., Rozenberg, G.: The many facets of natural computing. Commun. ACM 51(10), 72–83 (2008)CrossRefGoogle Scholar
  18. 18.
    Konieczny, P., Jóźwiak, L.: Minimal input support problem and algorithms to solve it. Eindhoven University of Thechnology Reaserch Report, pp. 1–55 (1995)Google Scholar
  19. 19.
    Păun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Păun, G.: Membrane Computing: An Introduction. Natural Computing Series. Springer, Berlin (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Sonveaux, P., Végran, F., Schroeder, T., Wergin, M.C., Verrax, J., Rabbani, Z.N., De Saedeleer, C.J., Kennedy, K.M., Diepart, C., Jordan, B.F., et al.: Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J. Clin. Investig. 118(12), 3930–3942 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di PisaPisaItaly

Personalised recommendations