References

  1. M. Aoki (1968): Control of large-scale dynamic systems by aggregation. IEEE Trans. Autom. Control 13(3), pp. 246–253, doi:10.1109/TAC.1968.1098900.
  2. Apache Commons Mathematics Library, http://commons.apache.org/proper/commons-math/.
  3. P. Auger, R. Bravo de la Parra, J. C. Poggiale, E. Sánchez & T. Nguyen-Huu (2008): Aggregation of Variables and Applications to Population Dynamics, pp. 209–263. Springer Berlin Heidelberg, Berlin, Heidelberg, doi:10.1007/978-3-540-78273-5_5.
  4. C. Baier, B. Engelen & M. E. Majster-Cederbaum (2000): Deciding Bisimilarity and Similarity for Probabilistic Processes. J. Comput. Syst. Sci. 60(1), pp. 187–231, doi:10.1006/jcss.1999.1683.
  5. C.W. Barrett, R. Sebastiani, S.A. Seshia & C. Tinelli (2009): Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications, chapter Satisfiability Modulo Theories. IOS Press, doi:10.3233/978-1-58603-929-5-825.
  6. M. H. ter Beek, A. Legay, A. Lluch-Lafuente & A. Vandin (2015): Statistical analysis of probabilistic models of software product lines with quantitative constraints. In: SPLC 2015. ACM, pp. 11–15, doi:10.1145/2791060.2791087.
  7. M. H. ter Beek, A. Legay, A. Lluch-Lafuente & A. Vandin (2016): Statistical Model Checking for Product Lines. In: ISoLA 2016, LNCS 9952, pp. 114–133, doi:10.1007/978-3-319-47166-2_8.
  8. L. Belzner, R. De Nicola, A. Vandin & M. Wirsing (2014): Reasoning (on) Service Component Ensembles in Rewriting Logic. In: Specification, Algebra, and Software, LNCS 8373. Springer, pp. 188–211, doi:10.1007/978-3-642-54624-2.
  9. M. L. Blinov, J. R. Faeder, B. Goldstein & W. S. Hlavacek (2004): BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20(17), pp. 3289–3291, doi:10.1093/bioinformatics/bth378.
  10. N. M. Borisov, N. I. Markevich, J. B. Hoek & B. N. Kholodenko (2005): Signaling through Receptors and Scaffolds: Independent Interactions Reduce Combinatorial Complexity. Biophysical Journal 89(2), pp. 951 – 966, doi:10.1529/biophysj.105.060533.
  11. F. van Breugel & J. Worrell (2006): Approximating and computing behavioural distances in probabilistic transition systems. TCS 360(1–3), pp. 373–385, doi:10.1016/j.tcs.2006.05.021.
  12. L. Calzone, F. Fages & S. Soliman (2006): BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14), pp. 1805–1807, doi:10.1093/bioinformatics/btl172.
  13. L. Cardelli (2008): On process rate semantics. TCS 391(3), pp. 190–215, doi:10.1016/j.tcs.2007.11.012.
  14. L. Cardelli (2014): Morphisms of reaction networks that couple structure to function. BMC Systems Biology 8(1), doi:10.1186/1752-0509-8-84.
  15. L. Cardelli, A. Csikász-Nagy, N. Dalchau, M. Tribastone & M. Tschaikowski (2016): Noise Reduction in Complex Biological Switches. Scientific Reports 6, pp. 20214, doi:10.1038/srep20214.
  16. L. Cardelli, R. D. Hernansaiz-Ballesteros, N. Dalchau & A. Csikász-Nagy (2017): Efficient Switches in Biology and Computer Science. PLOS Computational Biology 13(1), pp. 1–16, doi:10.1371/journal.pcbi.1005100.
  17. L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2015): Forward and Backward Bisimulations for Chemical Reaction Networks. In: CONCUR, pp. 226–239, doi:10.4230/LIPIcs.CONCUR.2015.226.
  18. L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2016): Comparing Chemical Reaction Networks: A Categorical and Algorithmic Perspective. In: LICS 2016, pp. 485–494, doi:10.1145/2933575.2935318.
  19. L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2016): Efficient Syntax-Driven Lumping of Differential Equations. In: TACAS 2016, pp. 93–111, doi:10.1007/978-3-662-49674-9_6.
  20. L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2016): Symbolic computation of differential equivalences. In: POPL 2016, pp. 137–150, doi:10.1145/2837614.2837649.
  21. L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2017): ERODE: A Tool for the Evaluation and Reduction of Ordinary Differential Equations. In: TACAS 2017, pp. 310–328, doi:10.1007/978-3-662-54580-5_19.
  22. F. Ciocchetta & J. Hillston (2009): Bio-PEPA: A framework for the modelling and analysis of biological systems. TCS 410(33-34), pp. 3065–3084, doi:10.1016/j.tcs.2009.02.037.
  23. V. Danos & C. Laneve (2004): Formal molecular biology. TCS 325(1), pp. 69–110, doi:10.1016/j.tcs.2004.03.065.
  24. L. De Moura & N. Bjørner (2008): Z3: An Efficient SMT Solver. In: TACAS, pp. 337–340, doi:10.1007/978-3-540-78800-3_24.
  25. C. Dehnert, J.-P. Katoen & D. Parker (2013): SMT-Based Bisimulation Minimisation of Markov Models. In: VMCAI'13, LNCS 7737. Springer, pp. 28–47, doi:10.1007/978-3-642-35873-9_5.
  26. S. Derisavi, H. Hermanns & W.H. Sanders (2003): Optimal state-space lumping in Markov chains. Information Processing Letters 87(6), pp. 309–315, doi:10.1016/S0020-0190(03)00343-0.
  27. J. Desharnais, V. Gupta, R. Jagadeesan & P. Panangaden (2004): Metrics for labelled Markov processes. Theor. Comput. Sci. 318(3), pp. 323–354, doi:10.1016/j.tcs.2003.09.013.
  28. J. Feret, V. Danos, J. Krivine, R. Harmer & W. Fontana (2009): Internal coarse-graining of molecular systems. PNAS 106(16), pp. 6453–6458, doi:10.1073/pnas.0809908106.
  29. J. Feret, T. Henzinger, H. Koeppl & T. Petrov (2012): Lumpability abstractions of rule-based systems. TCS 431(0), pp. 137–164, doi:10.1016/j.tcs.2011.12.059.
  30. E. Florian, C. C. Friedel & R. Zimmer (2008): FERN - a Java framework for stochastic simulation and evaluation of reaction networks. BMC Bioinformatics 9(1), pp. 356, doi:10.1186/1471-2105-9-356.
  31. Microsoft GEC, http://research.microsoft.com/en-us/projects/gec/.
  32. D. T. Gillespie (1977): Exact Stochastic Simulation of Coupled Chemical Reactions. Journal of Physical Chemistry 81(25), pp. 2340–2361, doi:10.1021/j100540a008.
  33. S. Gilmore, M. Tribastone & A. Vandin (2014): An Analysis Pathway for the Quantitative Evaluation of Public Transport Systems. In: IFM 2014, pp. 71–86, doi:10.1007/978-3-319-10181-1_5.
  34. V. Gupta, R. Jagadeesan & P. Panangaden (2006): Approximate reasoning for real-time probabilistic processes. LMCS 2(1), doi:10.2168/LMCS-2(1:4)2006.
  35. R. A. Hayden & J. T. Bradley (2010): A fluid analysis framework for a Markovian process algebra. TCS 411(22-24), pp. 2260–2297, doi:10.1016/j.tcs.2010.02.001.
  36. J. Hillston, M. Tribastone & S. Gilmore (2011): Stochastic Process Algebras: From Individuals to Populations. The Computer Journal, doi:10.1093/comjnl/bxr094.
  37. G. Iacobelli & M. Tribastone (2013): Lumpability of fluid models with heterogeneous agent types. In: DSN, pp. 1–11, doi:10.1109/DSN.2013.6575346.
  38. G. Iacobelli, M. Tribastone & A. Vandin (2015): Differential Bisimulation for a Markovian Process Algebra. In: MFCS 2015, pp. 293–306, doi:10.1007/978-3-662-48057-1_23.
  39. N. Israeli & N. Goldenfeld (2006): Coarse-graining of cellular automata, emergence, and the predictability of complex systems. Phys. Rev. E 73, pp. 026203, doi:10.1103/PhysRevE.73.026203.
  40. Y. Iwasa, V. Andreasen & S. Levin (1987): Aggregation in model ecosystems. I. Perfect aggregation. Ecological Modelling 37(3-4), pp. 287–302, doi:10.1016/0304-3800(87)90030-5.
  41. M.Z. Kwiatkowska, G. Norman & D. Parker (2011): PRISM 4.0: Verification of Probabilistic Real-Time Systems. In: CAV, pp. 585–591, doi:10.1007/978-3-642-22110-1_47.
  42. M. Kwiatkowski & I. Stark (2008): The Continuous pi-Calculus: A Process Algebra for Biochemical Modelling. In: CMSB, pp. 103–122, doi:10.1007/978-3-540-88562-7_11.
  43. K.G. Larsen, R. Mardare & P. Panangaden (2012): Taking It to the Limit: Approximate Reasoning for Markov Processes. In: MFCS, pp. 681–692, doi:10.1007/978-3-642-32589-2_59.
  44. K.G. Larsen & A. Skou (1991): Bisimulation through probabilistic testing. Information and Computation 94(1), pp. 1–28, doi:10.1016/0890-5401(91)90030-6.
  45. A. Legay, B. Delahaye & S. Bensalem (2010): Statistical model checking: An overview. In: RV 2010. Springer, pp. 122–135, doi:10.1007/978-3-642-16612-9_11.
  46. G. Li & H. Rabitz (1989): A general analysis of exact lumping in chemical kinetics. Chemical Engineering Science 44(6), pp. 1413–1430, doi:10.1016/0009-2509(89)85014-6.
  47. J. D. Murray (2002): Mathematical Biology I: An Introduction, 3rd edition.. Springer, doi:10.1007/b98868.
  48. M. Nagasaki, S. Onami, S. Miyano & H. Kitano (1999): Bio-calculus: Its Concept and Molecular Interaction. Genome Informatics 10, pp. 133–143, doi:10.11234/gi1990.10.133.
  49. M. S. Okino & M. L. Mavrovouniotis (1998): Simplification of Mathematical Models of Chemical Reaction Systems. Chemical Reviews 2(98), pp. 391–408, doi:10.1021/cr950223l.
  50. R. Paige & R. Tarjan (1987): Three Partition Refinement Algorithms. SIAM Journal on Computing 16(6), pp. 973–989, doi:10.1137/0216062.
  51. G. J. Pappas (2003): Bisimilar linear systems. Automatica 39(12), pp. 2035–2047, doi:10.1016/j.automatica.2003.07.003.
  52. M. Pedersen & G. D. Plotkin (2010): A Language for Biochemical Systems: Design and Formal Specification. In: Trans. Comp. Systems Biology XII. Springer, pp. 77–145, doi:10.1007/978-3-642-11712-1_3.
  53. D. Pianini, S. Sebastio & A. Vandin (2014): Distributed statistical analysis of complex systems modeled through a chemical metaphor. In: HPCS 2014. IEEE, pp. 416–423, doi:10.1109/HPCSim.2014.6903715.
  54. A. Di Pierro, C. Hankin & H. Wiklicky (2003): Quantitative Relations and Approximate Process Equivalences. In: CONCUR, pp. 498–512, doi:10.1007/978-3-540-45187-7_33.
  55. S. Sebastio & A. Vandin (2013): MultiVeStA: Statistical model checking for discrete event simulators. In: Valuetools 2013. ACM, pp. 310–315, doi:10.4108/icst.valuetools.2013.254377.
  56. S. Tognazzi, M. Tribastone, M. Tschaikowski & A. Vandin (2017): EGAC: A Genetic Algorithm to Compare Chemical Reaction Networks. In: GECCO 2017 (to appear), doi:10.1145/3071178.3071265.
  57. J. Toth, G. Li, H. Rabitz & A.S. Tomlin (1997): The Effect of Lumping and Expanding on Kinetic Differential Equations. SIAM JAM 57(6), pp. 1531–1556, doi:10.1137/S0036139995293294.
  58. M. Tribastone (2013): A Fluid Model for Layered Queueing Networks. IEEE Transactions on Software Engineering 39(6), pp. 744–756, doi:10.1109/TSE.2012.66.
  59. M. Tribastone, S. Gilmore & J. Hillston (2012): Scalable Differential Analysis of Process Algebra Models. IEEE Trans. Software Eng. 38(1), pp. 205–219, doi:10.1109/TSE.2010.82.
  60. M. Tschaikowski & M. Tribastone (2012): Exact fluid lumpability for Markovian process algebra. In: CONCUR, LNCS, pp. 380–394, doi:10.1007/978-3-642-32940-1_27.
  61. M. Tschaikowski & M. Tribastone (2014): Exact fluid lumpability in Markovian process algebra. TCS 538, pp. 140–166, doi:10.1016/j.tcs.2013.07.029.
  62. M. Tschaikowski & M. Tribastone (2016): Approximate Reduction of Heterogenous Nonlinear Models With Differential Hulls. IEEE Trans. Automat. Contr. 61(4), pp. 1099–1104, doi:10.1109/TAC.2015.2457172.
  63. T. Turanyi & A. Tomlin (2014): Analysis of Kinetic Reaction Mechanisms. Springer, doi:10.1007/978-3-662-44562-4.
  64. A. Valmari & G. Franceschinis (2010): Simple O(mologn) Time Markov Chain Lumping. In: TACAS, pp. 38–52, doi:10.1007/978-3-642-12002-2_4.
  65. A. Vandin & M. Tribastone (2016): Quantitative Abstractions for Collective Adaptive Systems. In: SFM 2016, Bertinoro Summer School, pp. 202–232, doi:10.1007/978-3-319-34096-8_7.
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