Metabolic P System Flux Regulation by Artificial Neural Networks

  • Alberto Castellini
  • Vincenzo Manca
  • Yasuhiro Suzuki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5957)


Metabolic P systems are an extension of P systems employed for modeling biochemical systems in a discrete and deterministic perspective. The generation of MP models from observed data of biochemical system dynamics is a hard problem which requires to solve several subproblems. Among them, flux tuners discovery aims to identify substances and parameters involved in tuning each reaction flux. In this paper we propose a new technique for discovering flux tuners by means of neural networks. This methodology, based on backpropagation with weight elimination for neural network training and on an heuristic algorithm for computing tuning indexes, has achieved encouraging results in a synthetic case study.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aczel, A.D., Sounderpandian, J.: Complete Business Statistics. McGraw-Hill, New York (2006)Google Scholar
  2. 2.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  3. 3.
    Castellini, A., Franco, G., Manca, V.: Hybrid functional Petri nets as MP systems. Natural Computing 9121 (2009), doi:10.1007/s11047-009-9121-4Google Scholar
  4. 4.
    Castellini, A., Franco, G., Manca, V.: Toward a representation of hybrid functional Petri nets by MP systems. In: Suzuki, Y., et al. (eds.) Natural Computing. PICT, vol. 1, pp. 28–37. Springer, Japan (2009)CrossRefGoogle Scholar
  5. 5.
    Castellini, A., Manca, V.: Learning regulation functions of metabolic systems by artificial neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009. ACM Publisher, New York (2009)Google Scholar
  6. 6.
    Castellini, A., Manca, V.: MetaPlab: A computational framework for metabolic P systems. In: Corne, D.W., Frisco, P., Paun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2008. LNCS, vol. 5391, pp. 157–168. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ciobanu, G., Păun, G., Pérez-Jiménez, M.J. (eds.): Applications of Membrane Computing. Springer, Berlin (2006)Google Scholar
  8. 8.
    du Jardin, P.: Bankruptcy prediction and neural networks: the contribution of variable selection methods. In: Proceedings of ESTSP 2008, pp. 271–284 (2008)Google Scholar
  9. 9.
    Fisher, J., Henzinger, T.A.: Executable cell biology. Nature Biotechnology 25(11), 1239–1249 (2007)CrossRefGoogle Scholar
  10. 10.
    Fontana, F., Manca, V.: Discrete solutions to differential equations by metabolic P systems. Theoretical Computer Science 372(2-3), 165–182 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3), 183–192 (1989)CrossRefGoogle Scholar
  12. 12.
    Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. of Computational Physics 22, 403–434 (1976)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  14. 14.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  15. 15.
    Krasnogor, N., Smith, J.E.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans. Evolutionary Computation 9(5), 474–488 (2005)CrossRefGoogle Scholar
  16. 16.
    Leray, P., Gallinari, P.: Feature selection with neural networks. Behaviormetrika 26, 16–16 (1998)Google Scholar
  17. 17.
    Lindenmayer, A.: Mathematical models for cellular interactions in development I. Filaments with one-sided inputs. J. of Theoretical Biology 18(3), 280–299 (1968)CrossRefGoogle Scholar
  18. 18.
    Manca, V.: The Metabolic algorithm: Principles and applications. Theoretical Computer Science 404, 142–157 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Manca, V.: Fundamentals of metabolic P systems. In: Păun, G., et al. (eds.) Handbook of Membrane Computing, ch. 16. Oxford University Press, Oxford (2009)Google Scholar
  20. 20.
    Manca, V.: Log-gain principles for metabolic P systems. In: Condon, A., et al. (eds.) Algorithmic Bioprocesses. Natural Computing Series, ch. 28. Springer, Heidelberg (2009)Google Scholar
  21. 21.
    Manca, V.: Metabolic P dynamics. In: Păun, G., et al. (eds.) Handbook of Membrane Computing, ch. 17. Oxford University Press, Oxford (2009)Google Scholar
  22. 22.
    Manca, V., Bianco, L.: Biological networks in metabolic P systems. BioSystems 91(3), 489–498 (2008)CrossRefGoogle Scholar
  23. 23.
    Manca, V., Bianco, L., Fontana, F.: Evolutions and oscillations of P systems: Applications to biochemical phenomena. In: Mauri, G., Păun, G., Jesús Pérez-Jímenez, M., Rozenberg, G., Salomaa, A. (eds.) WMC 2004. LNCS, vol. 3365, pp. 63–84. Springer, Heidelberg (2005)Google Scholar
  24. 24.
    Manca, V., Castellini, A., Franco, G., Marchetti, L., Pagliarini, R.: Metaplab 1.1 user guide (2009),
  25. 25.
    Pérez-Jiménez, M.J., Romero-Campero, F.J.: P systems: a new computational modelling tool for systems biology. In: Priami, C., Plotkin, G. (eds.) Transactions on Computational Systems Biology VI. LNCS (LNBI), vol. 4220, pp. 176–197. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Păun, G.: Membrane Computing. An Introduction. Springer, Berlin (2002)zbMATHGoogle Scholar
  28. 28.
    The P Systems Web Site,
  29. 29.
    Suzuki, Y., Fujiwara, Y., Takabayashi, J., Tanaka, H.: Artificial life applications of a class of P systems: Abstract rewriting systems on multisets. In: Calude, C.S., Pun, G., Rozenberg, G., Salomaa, A. (eds.) Multiset Processing. LNCS, vol. 2235, pp. 299–346. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  30. 30.
    Suzuki, Y., Tanaka, H.: Modeling p53 signaling pathways by using multiset processing. In: [7], pp. 203–214Google Scholar
  31. 31.
    Voit, E.O.: Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists. Cambridge University Press, Cambridge (2000)Google Scholar
  32. 32.
  33. 33.
    Weigend, A.S., Rumelhart, D.E., Huberman, B.A.: Generalization by weight-elimination with application to forecasting. In: Lippmann, R., et al. (eds.) NIPS, pp. 875–882. Morgan Kaufmann, San Francisco (1990)Google Scholar
  34. 34.
    Yacoub, M., Bennani, Y.: HVS: A heuristic for variable selection in multilayer artificial neural network classifier. In: Proc. of ANNIE 1997, pp. 527–532 (1997)Google Scholar
  35. 35.
    Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alberto Castellini
    • 1
  • Vincenzo Manca
    • 1
  • Yasuhiro Suzuki
    • 2
  1. 1.Dept. of Computer ScienceVerona UniversityVeronaItaly
  2. 2.Dept. of Complex Systems ScienceNagoya UniversityNagoyaJapan

Personalised recommendations