Skip to main content
SpringerLink
Log in

Metabolic P System Flux Regulation by Artificial Neural Networks

  • Conference paper
Membrane Computing (WMC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5957))

Included in the following conference series:

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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.

References

  1. Aczel, A.D., Sounderpandian, J.: Complete Business Statistics. McGraw-Hill, New York (2006)

    Google Scholar 

  2. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  3. Castellini, A., Franco, G., Manca, V.: Hybrid functional Petri nets as MP systems. Natural Computing 9121 (2009), doi:10.1007/s11047-009-9121-4

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  7. Ciobanu, G., Păun, G., Pérez-Jiménez, M.J. (eds.): Applications of Membrane Computing. Springer, Berlin (2006)

    Google Scholar 

  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. Fisher, J., Henzinger, T.A.: Executable cell biology. Nature Biotechnology 25(11), 1239–1249 (2007)

    Article  Google Scholar 

  10. Fontana, F., Manca, V.: Discrete solutions to differential equations by metabolic P systems. Theoretical Computer Science 372(2-3), 165–182 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  11. Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3), 183–192 (1989)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  16. Leray, P., Gallinari, P.: Feature selection with neural networks. Behaviormetrika 26, 16–16 (1998)

    Google Scholar 

  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)

    Article  Google Scholar 

  18. Manca, V.: The Metabolic algorithm: Principles and applications. Theoretical Computer Science 404, 142–157 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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. 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. Manca, V., Bianco, L.: Biological networks in metabolic P systems. BioSystems 91(3), 489–498 (2008)

    Article  Google Scholar 

  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. Manca, V., Castellini, A., Franco, G., Marchetti, L., Pagliarini, R.: Metaplab 1.1 user guide (2009), http://mplab.scienze.univr.it

  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)

    Chapter  Google Scholar 

  26. Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  27. Păun, G.: Membrane Computing. An Introduction. Springer, Berlin (2002)

    MATH  Google Scholar 

  28. The P Systems Web Site, http://ppage.psystems.eu/

  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)

    Chapter  Google Scholar 

  30. Suzuki, Y., Tanaka, H.: Modeling p53 signaling pathways by using multiset processing. In: [7], pp. 203–214

    Google Scholar 

  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. MetaPlab website, http://mplab.scienze.univr.it

  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. 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. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science, Verona University, Strada Le Grazie 15, 37134, Verona, Italy

    Alberto Castellini & Vincenzo Manca

  2. Dept. of Complex Systems Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Yasuhiro Suzuki

Authors
  1. Alberto Castellini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincenzo Manca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasuhiro Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Mathematics of the Romanian Academy, PO Box 1-764, 014700, Bucharest, Romania

    Gheorghe Păun

  2. Research Group on Natural Computing, University of Sevilla, Sevilla, Spain

    Mario J. Pérez-Jiménez

  3. Research Group on Natural Computing, University of Sevilla, Spain

    Agustín Riscos-Núñez

  4. Leiden Center of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333, Leiden, CA, The Netherlands

    Grzegorz Rozenberg

  5. Turku Centre for Computer Science (TUCS), Leminkäisenkatu 14, 20520, Turku, Finland

    Arto Salomaa

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Castellini, A., Manca, V., Suzuki, Y. (2010). Metabolic P System Flux Regulation by Artificial Neural Networks. In: Păun, G., Pérez-Jiménez, M.J., Riscos-Núñez, A., Rozenberg, G., Salomaa, A. (eds) Membrane Computing. WMC 2009. Lecture Notes in Computer Science, vol 5957. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11467-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11467-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11466-3

  • Online ISBN: 978-3-642-11467-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation