A Multiscale Modeling Framework Based on P Systems

  • Francisco José Romero-Campero
  • Jamie Twycross
  • Hongqing Cao
  • Jonathan Blakes
  • Natalio Krasnogor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5391)


Cellular systems present a highly complex organization at different scales including the molecular, cellular and colony levels. The complexity at each one of these levels is tightly interrelated. Integrative systems biology aims to obtain a deeper understanding of cellular systems by focusing on the systemic and systematic integration of the different levels of organization in cellular systems.

The different approaches in cellular modeling within systems biology have been classified into mathematical and computational frameworks. Specifically, the methodology to develop computational models has been recently called executable biology since it produces executable algorithms whose computations resemble the evolution of cellular systems.

In this work we present P systems as a multiscale modeling framework within executable biology. P system models explicitly specify the molecular, cellular and colony levels in cellular systems in a relevant and understandable manner. Molecular species and their structure are represented by objects or strings, compartmentalization is described using membrane structures and finally cellular colonies and tissues are modeled as a collection of interacting individual P systems.

The interactions between the components of cellular systems are described using rewriting rules. These rules can in turn be grouped together into modules to characterize specific cellular processes. One of our current research lines focuses on the design of cell systems biology models exhibiting a prefixed behavior through the automatic assembly of these cellular modules. Our approach is equally applicable to synthetic as well as systems biology.


Molecular Species Cellular System Gene Expression Control Modeling Principle Colony Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alon, U.: Network motifs: theory and experimental approaches. Nature Reviews Genetics 8, 450–461 (2007)CrossRefGoogle Scholar
  2. 2.
    Bernardini, F., Gheorghe, M., Krasnogor, N.: Quorum sensing P systems. Theoretical Computer Sci. 371, 20–33 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G., Colombo, S., Martegani, E.: Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools. Journal of Biotechnology 133, 377–385 (2008)CrossRefGoogle Scholar
  4. 4.
    Bianco, L., Fontana, F., Manca, V.: P systems with reaction maps. Intern. J. Foundations of Computer Sci. 17, 27–48 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Ciobanu, G., Pan, L., Păun, G., Pérez-Jiménez, M.J.: P systems with minimal parallelism. Theoretical Computer Sci. 378, 117–130 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fisher, J., Henzinger, T.A.: Executable cell biology. Nature Biotechnology 25, 1239–1249 (2007)CrossRefGoogle Scholar
  7. 7.
    Fontana, F., Manca, V.: Discrete solutions to differential equations by metabolic P systems. Theoretical Computer Sci. 372, 165–182 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Freund, R.: P systems working in the sequential mode on arrays and strings. Int. J. Found. Comput. Sci. 16, 663–682 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)CrossRefGoogle Scholar
  10. 10.
    Heiner, M., Gilbert, D., Donaldson, R.: Petri nets for systems and synthetic biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Krasnogor, N., Gheorghe, M., Terrazas, G., Diggle, S., Williams, P., Camara, M.: An appealing computational mechanism drawn from bacterial quorum sensing. Bulletin of the EATCS 85, 135–148 (2005)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Păun, A., Jesús Pérez-Jímenez, M., Romero-Campero, F.J.: Modeling signal transduction using P systems. In: Hoogeboom, H.J., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2006. LNCS, vol. 4361, pp. 100–122. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Păun, G.: Computing with membranes. J. Computer and System Sci. 61, 108–143 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Păun, G.: Membrane Computing: An Introduction. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  15. 15.
    Păun, G., Romero-Campero, F.J.: Membrane computing as a modeling framework. Cellular systems case studies. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 168–214. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Jesús Pérez-Jímenez, M., 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
  17. 17.
    Pescini, D., Besozzi, D., Mauri, G., Zandron, C.: Dynamical probabilistic P systems. Intern. J. Foundations of Computer Sci. 17, 183–204 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ptashne, M., Gann, A.: Genes and Signals. Cold Spring Harbor Laboratory Press (2002)Google Scholar
  19. 19.
    Regev, A., Shapiro, E.: The π-calculus as an abstraction for biomolecular systems. Modelling in Molecular Biology, 1–50 (2004)Google Scholar
  20. 20.
    Romero-Campero, F.J., Pérez-Jiménez, M.J.: Modelling gene expression control using P systems: the Lac Operon, a case study. BioSystems 91, 438–457 (2008)CrossRefGoogle Scholar
  21. 21.
    Romero-Campero, F.J., Pérez-Jiménez, M.J.: A model of the quorum sensing system in Vibrio fischeri using P systems. Artificial Life 14, 1–15 (2008)CrossRefGoogle Scholar
  22. 22.
    Romero-Campero, F.J., Cao, H., Cámara, M., Krasnogor, N.: Structure and parameter estimation for cell systems biology models. In: Proc. of the Genetic and Evolutionary Computation Conference, Atlanta, USA, pp. 331–338 (2008)Google Scholar
  23. 23.
    Romero-Campero, F.J., Twycross, J., Cámara, M., Bennett, M., Gheorghe, M., Krasnogor, N.: Modular assembly of cell systems biology models using P systems (submitted)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francisco José Romero-Campero
    • 1
  • Jamie Twycross
    • 1
    • 2
  • Hongqing Cao
    • 1
  • Jonathan Blakes
    • 1
  • Natalio Krasnogor
    • 1
  1. 1.Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, Jubilee CampusUniversity of NottinghamNottinghamUnited Kingdom
  2. 2.Centre for Plant Integrative BiologyUniversity of NottinghamNottinghamUnited Kingdom

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