References

  1. M. Antoniotti, C. Piazza, A. Policriti, M. Simeoni & B. Mishra (2004): Taming the complexity of biochemical models through bisimulation and collapsing: theory and practice. Theoretical Computer Science 325, pp. 45–67, doi:10.1016/j.tcs.2004.03.064.
  2. C. Baier, J.-P. Katoen, H. Hermanns & V. Wolf (2005): Comparative branching-time semantics for Markov chains. Information and Computation 200, pp. 149–214, doi:10.1016/j.ic.2005.03.001.
  3. R. Barbuti, A. Maggiolo-Schettini, P. Milazzo & A. Troina (2008): Bisimulation in calculi modelling membranes. Formal Aspects of Computing 20, pp. 351–377, doi:10.1007/s00165-008-0071-x.
  4. A. Blakemore & S.K. Tripathi (1993): Automated time scale decomposition and analysis of stochastic Petri nets. In: -.1667emProceedings -.1667emof -.1667emPetri -.1667emNets -.1667emand -.1667emPerformance -.1667emModels, pp. 248–257, doi:10.1109/PNPM.1993.393446.
  5. R. Blossey, L. Cardelli & A. Phillips (2006): A compositional approach to the stochastic dynamics of gene networks. In: TCSB IV, LNCS 3939. Springer, pp. 99–122, doi:10.1007/11732488_10.
  6. A. Bobbio & K.S. Trivedi (1986): An Aggregation Technique for the Transient Analysis of Stiff Markov Chains. IEEE Transaction on Computers 35, pp. 803–814, doi:10.1109/TC.1986.1676840.
  7. J. Borghans, R. de Boer & L. Segel (1996): Extending the quasi-steady state approximation by changing variables. Bulletin of Mathematical Biology 58, pp. 43–63, doi:10.1007/BF02458281.
  8. L. Bortolussi & A. Policriti (2008): Modeling biological systems in stochastic Concurrent Constraint Programming. Constraints 13, pp. 66–90, doi:10.1007/s10601-007-9034-8.
  9. Y. Cao, D. T. Gillespie & L.R. Petzold (2005): Accelerated stochastic simulation of the stiff enzyme-substrate reaction. Journal of Chemical Physics 123(14), pp. 144917, doi:10.1063/1.2052596.
  10. Y. Cao, D. T. Gillespie & L.R. Petzold (2005): The slow-scale stochastic simulation algorithm. Journal of Chemical Physics 122(1), pp. 14116, doi:10.1063/1.1824902.
  11. G. Ciobanu & B. Aman (2008): On the relationship between membranes and ambients. BioSystems 91, pp. 515–530, doi:10.1016/j.biosystems.2007.01.006.
  12. F. Ciocchetta & M. L. Guerriero (2009): Modelling biological compartments in Bio-PEPA. Electronic Notes in Theoretical Computer Science 227, pp. 77–95, doi:10.1016/j.entcs.2008.12.105.
  13. F. Ciocchetta & J. Hillston (2008): Process Algebras in Systems Biology. In: Formal Methods for Computational Systems Biology (SFM08), LNCS 5016, pp. 265–312, doi:10.1007/978-3-540-68894-5_8.
  14. F. Ciocchetta & J. Hillston (2009): Bio-PEPA: a framework for the modelling and analysis of biological systems. Theoretical Computer Science, 410(33-34), pp. 3065–3084, doi:10.1016/j.tcs.2009.02.037.
  15. A. Clark, V. Galpin, S. Gilmore, M.L. Guerriero & J. Hillston (2012): Formal methods for checking the consistency of biological models. In: Advances in Systems Biology, Advances in Experimental Medicine and Biology 736. Springer.
  16. Allan Clark, Stephen Gilmore, Maria Luisa Guerriero & Peter Kemper (2010): On verifying Bio-PEPA models. In: Proceedings of CMSB 2010. ACM Press, pp. 23–32, doi:10.1145/1839764.1839769.
  17. P.J. Courtois (1977): Decomposability: Queueing and Computer System Applications. Academic Press.
  18. V. Danos & C. Laneve (2004): Formal molecular biology. Theoretical Computer Science 325, pp. 69–110, doi:10.1016/j.tcs.2004.03.065.
  19. V. Galpin (2011): Equivalences for a biological process algebra. Theoretical Computer Science, to appear,, doi:10.1016/j.tcs.2011.07.006.
  20. V. Galpin & J. Hillston (2011): A semantic equivalence for Bio-PEPA based on discretisation of continuous values. Theoretical Computer Science 412, pp. 2142–2161, doi:10.1016/j.tcs.2011.01.007.
  21. D. T. Gillespie (1997): Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), pp. 2340–2361, doi:10.1021/j100540a008.
  22. C.A. Gómez-Uribe, G.C. Verghese & A.R. Tzafriri (2008): Enhanced identification and exploitation of time scales for model reduction in stochastic chemical kinetics. Journal of Chemical Physics 129, doi:10.1063/1.3050350.
  23. M. Heiner, D. Gilbert & R. Donaldson (2008): Petri nets for Systems and Synthetic Biology. In: Formal Methods for Computational Systems Biology (SFM08), LNCS 5016, pp. 215–264, doi:10.1007/978-3-540-68894-5_7.
  24. J. Hillston (1996): A compositional approach to performance modelling. CUP.
  25. J. Hillston & V. Mertsiotakis (1995): A Simple Time Scale Decomposition Technique for Stochastic Process Algebras. Computer Journal 38, pp. 566–577, doi:10.1093/comjnl/38.7.566.
  26. M. Kwiatkowski & I. Stark (2008): The Continuous π-Calculus: A Process Algebra for Biochemical Modelling. In: Proceedings of CMSB 2008, LNCS 5307, pp. 103–122, doi:10.1007/978-3-540-88562-7_11.
  27. C. Laneve & F. Tarissan (2008): A simple calculus for proteins and cells. Theoretical Computer Science 404, pp. 127–141, doi:10.1016/j.tcs.2008.04.011.
  28. L. Michaelis & M. Menten (1913): Die Kinetik der Invertinwirkung. Biochemistry Zeitung 49, pp. 333–369.
  29. R. Milner (1989): Communication and concurrency. Prentice Hall.
  30. M.C. Pinto, L. Foss, J.C.M. Mombach & L. Ribeiro (2007): Modelling, property verification and behavioural equivalence of lactose operon regulation. Computers in Biology and Medicine 37, pp. 134–148, doi:10.1016/j.compbiomed.2006.01.006.
  31. C. Priami & P. Quaglia (2004): Beta Binders for Biological Interactions. In: Proceedings of CMSB 2004, LNCS 3082, pp. 20–33, doi:10.1007/978-3-540-25974-9_3.
  32. C. Priami, A. Regev, E. Shapiro & W. Silverman (2001): Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Information Processing Letters 80, pp. 25–31, doi:10.1016/S0020-0190(01)00214-9.
  33. A. Regev, E. Panina, W. Silverman, L. Cardelli & E. Shapiro (2004): BioAmbients: an abstraction for biological compartments. Theoretical Computer Science 325, pp. 141–167, doi:10.1016/j.tcs.2004.03.061.
  34. A. Regev & E. Shapiro (2002): Cellular abstractions: Cells as computation. Nature 419, pp. 343, doi:10.1038/419343a.
  35. K.R. Sanft, D.T. Gillespie & L.R.Petzold (2010): Legitimacy of the stochastic Michaelis-Menten approximation. IET Systems Biology 5, pp. 58–69, doi:10.1049/iet-syb.2009.0057.
  36. I. H. Segel (1993): Enzyme Kinetics: Behaviour and Analysis of Rapid Equilibrium And Steady-State Enzyme Systems. Wiley Blackwell.
  37. L.A. Segel & M. Slemrod (1989): The Quasi-Steady-State Assumption: A Case Study in Perturbation. SIAM Review 31, pp. 446–477, doi:10.1137/1031091.
  38. A. Zagaris, H.G. Kaper & T.J. Kaper (2004): -.1667emAnalysis -.1667emof -.1667emthe -.1667emComputational -.1667emSingular -.1667emPerturbation -.1667emReduction -.1667emMethod -.1667emfor -.1667emChemical -.1667emKinetics. -.1667emJournal -.1667emof -.1667emNonlinear -.1667emScience -.1667em14, pp. -.1667em59–91, doi:10.1007/s00332-003-0582-9.
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