Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017 (2017), Article ID 1232868, 12 pages
https://doi.org/10.1155/2017/1232868
Research Article

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

1The Microsoft Research–University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, No. 1, 38068 Rovereto, Italy
2Department of Computer Science, University of Pisa, Pisa, Italy

Correspondence should be addressed to Luca Marchetti

Received 12 May 2017; Revised 18 August 2017; Accepted 19 November 2017; Published 13 December 2017

Academic Editor: Valeri Mladenov

Copyright © 2017 Luca Marchetti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Kitano, “Computational systems biology,” Nature, vol. 420, no. 6912, pp. 206–210, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Kitano, “Systems biology: a brief overview,” Science, vol. 295, no. 5560, pp. 1662–1664, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Hood and D. Galas, “The digital code of DNA,” Nature, vol. 421, no. 6921, pp. 444–448, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. A. P. Heath and L. E. Kavraki, “Computational challenges in systems biology,” Computer Science Review, vol. 3, no. 1, pp. 1–17, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Priami, “Algorithmic systems biology,” Communications of the ACM, vol. 52, no. 5, pp. 80–88, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Priami and M. J. Morine, Analysis of Biological Systems, Imperial College Press, London, UK, 2015. View at Publisher · View at Google Scholar
  7. L. Marchetti, C. Priami, and V. H. Thanh, Simulation Algorithms for Computational Systems Biology, Springer International Publishing, Berlin, Germany, 2017.
  8. D. T. Gillespie, “Exact stochastic simulation of coupled chemical reactions,” The Journal of Physical Chemistry C, vol. 81, no. 25, pp. 2340–2361, 1977. View at Publisher · View at Google Scholar · View at Scopus
  9. M. A. Gibson and J. Bruck, “Efficient exact stochastic simulation of chemical systems with many species and many channels,” The Journal of Physical Chemistry A, vol. 104, no. 9, pp. 1876–1889, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. V. H. Thanh, C. Priami, and R. Zunino, “Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays,” The Journal of Chemical Physics, vol. 141, no. 13, Article ID 134116, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. D. T. Gillespie, “The Chemical Langevin equation,” The Journal of Chemical Physics, vol. 113, no. 1, pp. 297–306, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. D. T. Gillespie, “Approximate accelerated stochastic simulation of chemically reacting systems,” The Journal of Chemical Physics, vol. 115, no. 4, pp. 1716–1733, 2001. View at Publisher · View at Google Scholar · View at Scopus
  13. J. C. Butcher, Numerical Methods for Ordinary Differential Equations, John Wiley & Sons, New York, NY, USA, 2nd edition, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  14. J. Pahle, “Biochemical simulations: stochastic, approximate stochastic and hybrid approaches,” Briefings in Bioinformatics, vol. 10, no. 1, pp. 53–64, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Bentele and R. Eils, “General stochastic hybrid method for the simulation of chemical reaction processes in cells,” in Computational Methods in Systems Biology, V. Danos and V. Schachter, Eds., vol. 3082 of Lecture Notes in Computer Science, Springer, Berlin, Germany, 2005. View at Google Scholar · View at MathSciNet
  16. M. Herajy and M. Heiner, “Hybrid representation and simulation of stiff biochemical networks,” Nonlinear Analysis: Hybrid Systems, vol. 6, no. 4, pp. 942–959, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. R. Irizarry, “Stochastic simulation of population balance models with disparate time scales: hybrid strategies,” Chemical Engineering Science, vol. 66, no. 18, pp. 4059–4069, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Griffith, T. Courtney, J. Peccoud, and W. H. Sanders, “Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network,” Bioinformatics, vol. 22, no. 22, pp. 2782–2789, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Salis and Y. Kaznessis, “Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions,” The Journal of Chemical Physics, vol. 122, no. 5, Article ID 054103, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. E. L. Haseltine and J. B. Rawlings, “On the origins of approximations for stochastic chemical kinetics,” The Journal of Chemical Physics, vol. 123, no. 16, Article ID 164115, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Marchetti, C. Priami, and V. H. Thanh, “HRSSA—efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks,” Journal of Computational Physics, vol. 317, pp. 301–317, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  22. S. Hoops, R. Gauges, C. Lee et al., “COPASI—a COmplex PAthway SImulator,” Bioinformatics, vol. 22, no. 24, pp. 3067–3074, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. O. Kahramanoǧullari, G. Fantaccini, P. Lecca, D. Morpurgo, and C. Priami, “Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy,” PLoS ONE, vol. 7, no. 12, Article ID e50176, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. V. H. Thanh, R. Zunino, and C. Priami, “On the rejection-based algorithm for simulation and analysis of large-scale reaction networks,” The Journal of Chemical Physics, vol. 142, no. 24, Article ID 244106, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. V. H. Thanh and C. Priami, “Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm,” The Journal of Chemical Physics, vol. 143, no. 5, Article ID 054104, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. V. H. Thanh, R. Zunino, and C. Priami, “Efficient constant-time complexity algorithm for stochastic simulation of large reaction networks,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 3, pp. 657–667, 2017. View at Publisher · View at Google Scholar
  27. R. Lombardo and C. Priami, “Graphical modeling meets systems pharmacology,” Gene Regulation and Systems Biology, vol. 11, pp. 1–10, 2017. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Herajy, F. Liu, C. Rohr, and M. Heiner, “Snoopy’s hybrid simulator: a tool to construct and simulate hybrid biological models,” BMC Systems Biology, vol. 11, no. 71, 2017. View at Publisher · View at Google Scholar
  29. L. Chang and M. Karin, “Mammalian MAP kinase signalling cascades,” Nature, vol. 410, no. 6824, pp. 37–40, 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. R. J. Orton, O. E. Sturm, V. Vyshemirsky, M. Calder, D. R. Gilbert, and W. Kolch, “Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway,” Biochemical Journal, vol. 392, no. 2, pp. 249–261, 2005. View at Publisher · View at Google Scholar · View at Scopus