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ASSA-PBN: An Approximate Steady-State Analyser of Probabilistic Boolean Networks

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Automated Technology for Verification and Analysis (ATVA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9364))

Abstract

We present ASSA-PBN, a tool for approximate steady-state analysis of large probabilistic Boolean networks (PBNs). ASSA-PBN contains a constructor, a simulator, and an analyser which can approximately compute the steady-state probabilities of PBNs. For large PBNs, such approximate analysis is the only viable way to study their long-run behaviours. Experiments show that ASSA-PBN can handle large PBNs with a few thousands of nodes.

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References

  1. Shmulevich, I., Dougherty, E., Zhang, W.: From boolean to probabilistic boolean networks as models of genetic regulatory networks. Proc. IEEE 90(11), 1778–1792 (2002)

    Article  Google Scholar 

  2. Trairatphisan, P., Mizera, A., Pang, J., Tantar, A.A., Schneider, J., Sauter, T.: Recent development and biomedical applications of probabilistic boolean networks. Cell Commun. Signal. 11, 46 (2013)

    Article  Google Scholar 

  3. Shmulevich, I., Gluhovsky, I., Hashimoto, R., Dougherty, E., Zhang, W.: Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks. Comp. Funct. Genomics 4(6), 601–608 (2003)

    Article  Google Scholar 

  4. Trairatphisan, P., Mizera, A., Pang, J., Tantar, A.A., Sauter, T.: optPBN: An optimisation toolbox for probabilistic boolean networks. PLOS ONE 9(7), e98001 (2014)

    Article  Google Scholar 

  5. Vincent, J.M., Marchand, C.: On the exact simulation of functionals of stationary Markov chains. Linear Algebra Appl. 385, 285–310 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  6. Raftery, A., Lewis, S.: How many iterations in the Gibbs sampler? Bayesian Stat. 4, 763–773 (1992)

    Google Scholar 

  7. Mizera, A., Pang, J., Yuan, Q.: Reviving the two-state markov chain approach (technical report) (2015). Accessed on http://arxiv.org/abs/1501.01779

  8. Tafazzoli, A., Wilson, J., Lada, E., Steiger, N.: Skart: A skewness-and autoregression-adjusted batch-means procedure for simulation analysis. In: Proceedings of the 2008 Winter Simulation Conference, pp. 387–395 (2008)

    Google Scholar 

  9. Walker, A.: An efficient method for generating discrete random variables with general distributions. ACM Trans. Math. Softw. 3(3), 253–256 (1977)

    Article  MATH  Google Scholar 

  10. Shmulevich, I., Dougherty, E.R.: Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. SIAM Press, Philadelphia (2010)

    Book  MATH  Google Scholar 

  11. Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Propp, J.G., Wilson, D.: Exact sampling with coupled markov chains and applications to statistical mechanics. Random Struct. Algorithms 9(1), 223–252 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  13. El Rabih, D., Pekergin, N.: Statistical model checking using perfect simulation. In: Liu, Z., Ravn, A.P. (eds.) ATVA 2009. LNCS, vol. 5799, pp. 120–134. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Younes, H.L.S., Simmons, R.G.: Probabilistic verification of discrete event systems using acceptance sampling. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 223–235. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Sen, K., Viswanathan, M., Agha, G.: On statistical model checking of stochastic systems. In: Etessami, K., Rajamani, S.K. (eds.) CAV 2005. LNCS, vol. 3576, pp. 266–280. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Rohr, C.: Simulative model checking of steady state and time-unbounded temporal operators. Trans. Petri Nets Models Concurrency 8, 142–158 (2013)

    MATH  Google Scholar 

  17. Gelman, A., Rubin, D.: Inference from iterative simulation using multiple sequences. Stat. Sci. 7(4), 457–472 (1992)

    Article  Google Scholar 

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Acknowledgement

Qixia Yuan is supported by the National Research Fund, Luxembourg (grant 7814267).

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Correspondence to Andrzej Mizera .

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Mizera, A., Pang, J., Yuan, Q. (2015). ASSA-PBN: An Approximate Steady-State Analyser of Probabilistic Boolean Networks. In: Finkbeiner, B., Pu, G., Zhang, L. (eds) Automated Technology for Verification and Analysis. ATVA 2015. Lecture Notes in Computer Science(), vol 9364. Springer, Cham. https://doi.org/10.1007/978-3-319-24953-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-24953-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24952-0

  • Online ISBN: 978-3-319-24953-7

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