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Deterministic and stochastic P systems for modelling cellular processes

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Abstract

This paper presents two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological systems and illustrates their use through two case studies.

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References

  • Alberts B, Raff M (1997) Essential cell biology: an introduction to the molecular biology of the cell. Garland Science, New York

    Google Scholar 

  • Benner SA, Sismour M (2005) Synthetic biology. Nat Rev Genet 6:533–543

    Article  Google Scholar 

  • Bernardini F, Manca V (2003) Dynamical aspects of P systems. BioSystems 70(2):85–93

    Article  Google Scholar 

  • Bernardini F, Gheorghe M, Krasnogor N (2007a) Quorum sensing P systems. Theor Comput Sci 371(1–2):20–33

    Article  MATH  MathSciNet  Google Scholar 

  • Bernardini F, Gheorghe M, Romero-Campero FJ, Walkinshaw N (2007b) A hybrid approach to modeling biological systems. Lecture notes in computer science, vol 4860. Springer, Berlin, pp 138–159

    Google Scholar 

  • Castellini A, Manca V (2008) MetaPlab: a computational framework for metabolic P systems. Pre-proceedings WMC 2008, Edinburgh, UK

  • Castellini A, Manca V, Marchetti L (2008) MP systems and hybrid Petri nets. Stud Comput Intell 129: 53–62

    Article  Google Scholar 

  • Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(20):335–338

    Article  Google Scholar 

  • Ernst M, Cockrell J, Griswold W, Notkin D (2001) Dynamically discovering likely program invariants to support program evolution. IEEE Trans Softw Eng 27(2): 99–123

    Article  Google Scholar 

  • Fontana F, Manca V (2007) Discrete solutions to differential equations by metabolic P systems. Theor Comput Sci 372(2–3):165–182

    Article  MATH  MathSciNet  Google Scholar 

  • Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55

    Article  Google Scholar 

  • Goldbeter A (1991) A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS 88: 9107–9111

    Article  Google Scholar 

  • Krasnogor N, Gheorghe M, Terrazas G, Diggle S, Williams P, Camara M (2005) An appealing computational mechanism drawn from bacterial quorum sensing. Bull EATCS 85:135–148

    MATH  MathSciNet  Google Scholar 

  • Kwiatkowska M, Norman G, Parker D (2005) Probabilistic model checking in practice: case studies with PRISM. ACM SIGMETRICS Perform Eval Rev 32(4):16–21

    Article  Google Scholar 

  • Manca V (2007) Metabolic P systems for biochemical dynamics. Prog Nat Sci 17(4):384–391

    Article  MATH  MathSciNet  Google Scholar 

  • Manca V (2008a) Discrete simulations of biochemical dynamics. Lecture notes in computer science, vol 4848, Springer, Berlin, pp 231–235

    Google Scholar 

  • Manca V (2008b) The metabolic algorithm for P systems: principles and applications. Theor Comput Sci 404(1–2):142–157

    Article  MATH  MathSciNet  Google Scholar 

  • Manca V (2008c) Log-gain principles for metabolic P systems. Rozenberg’s Festschrift, Natural Computing Series, Springer, Berlin

  • Manca V (2009) Fundamentals of metabolic P systems. In: Paun G, Rozenberg G, Salomaa A (eds) Handbook of membrane computing, Chap. 16. Oxford University Press, Oxford

    Google Scholar 

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

    Article  Google Scholar 

  • Manca V, Bianco L, Fontana F (2005) Evolutions and oscillations of P systems: applications to biological phenomena. Lecture notes in computer science, vol 3365. Springer, Berlin, pp 63–84

    Google Scholar 

  • Manca V, Pagliarini R, Zorzan S (2008) A photosynthetic process modelled by a metabolic P system. Nat Comput (in press). doi: 10.1007/s11047-008-9104-x

  • Păun Gh (2000) Computing with membranes. J Comput Syst Sci 61:108–143

    Article  MATH  Google Scholar 

  • Păun Gh (2002) Membrane computing: an introduction. Springer, Berlin

    MATH  Google Scholar 

  • Pérez-Jiménez MJ, Romero-Campero FJ (2006) P Systems, a new computational modelling tool for systems biology. Transactions on Computational Systems Biology VI, LNBI, vol 4220, pp 176–197

  • Pescini D, Besozzi D, Mauri G, Zandron C (2006) Dynamical probabilistic P systems. Intern J Found Comput Sci 17:183–204

    Article  MATH  MathSciNet  Google Scholar 

  • Romero-Campero FJ, Pérez-Jiménez MJ (2008) A model of the quorum sensing system in Vibrio fischeri using P systems. Artif Life 14(1):1–15

    Article  Google Scholar 

  • Romero-Campero FJ, Gheorghe M, Ciobanu G, Bianco L, Pescini D, Pérez-Jiménez MJ, Ceterchi R (2006) Towards probabilistic model checking on P systems using PRISM. Lecture notes in computer science, vol 4361. Springer, Berlin, pp 477–495

    Google Scholar 

  • Romero-Campero FJ, Gheorghe M, Ciobanu G, Auld JM, Pérez-Jiménez MJ (2007) Cellular modelling using P systems and process algebra. Prog Nat Sci 17(4):375–383

    Article  MATH  Google Scholar 

  • Romero-Campero FJ, Cao H, Cámara M, Krasnogor N (2008) Structure and parameter estimation for cell systems biology models. In: Proceedings of the Genetic and Evolutionary Computation Conference, July 12–16, Atlanta, USA, pp 331–338

  • Romero-Campero FJ, Twycross J, Cámara M, Bennett M, Gheorghe M, Krasnogor N (2009) Modular assembly of cell systems biology models using P systems. Int J Found Comput Sci 20:427–442

    Google Scholar 

  • The P Systems Web Site (2008) http://www.ppage.psystems.eu

  • von Bertalanffy L (1967) General systems theory: foundations, developments, applications. George Braziller Inc., New York

    Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments. FJRC would like to acknowledge EPSRC grant EP/E017215/1 and BBSRC grants BB/F01855X/1 and BB/D019613/1.

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Authors and Affiliations

  1. Department of Computer Science, The University of Sheffield, Regent Court, Portobello Street, Sheffield, S1 4DP, UK

    Marian Gheorghe

  2. Department of Computer Science, The University of Verona, Strada Le Grazie 15, 37134, Verona, Italy

    Vincenzo Manca

  3. School of Computer Science, The University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK

    Francisco J. Romero-Campero

Authors
  1. Marian Gheorghe
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  2. Vincenzo Manca
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  3. Francisco J. Romero-Campero
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Corresponding author

Correspondence to Marian Gheorghe.

Additional information

M. Gheorghe, V. Manca, and F. J. Romero-Campero have equally contributed to this study.

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Gheorghe, M., Manca, V. & Romero-Campero, F.J. Deterministic and stochastic P systems for modelling cellular processes. Nat Comput 9, 457–473 (2010). https://doi.org/10.1007/s11047-009-9158-4

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