Skip to main content

Simulation Methods in Systems Biology

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5016))

Abstract

This chapter reviews the theory of stochastic chemical kinetics and several simulation methods that are based on that theory. An effort is made to delineate the logical connections among the major elements of the theory, such as the chemical master equation, the stochastic simulation algorithm, tau-leaping, the chemical Langevin equation, the chemical Fokker-Planck equation, and the deterministic reaction rate equation. Focused presentations are given of two approximate simulation strategies that aim to improve simulation efficiency for systems with “multiscale” complications of the kind that are often encountered in cellular systems: The first, explicit tau-leaping, deals with systems that have a wide range of molecular populations. The second, the slow-scale stochastic simulation algorithm, is designed for systems that have a wide range of reaction rates. The latter procedure is shown to provide a stochastic generalization of the Michaelis-Menten analysis of the enzyme-substrate reaction set.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gillespie, D.: Stochastic chemical kinetics. In: Yip, S. (ed.) Handbook of Materials Modeling, pp. 1735–1752. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Gillespie, D., Petzold, L.: Numerical simulation for biochemical kinetics. In: Szallasi, Z., Stelling, J., Periwal, V. (eds.) System Modeling in Cellular Biology, pp. 331–353. MIT Press, Cambridge (2006)

    Google Scholar 

  3. Gillespie, D.: Stochastic simulation of chemical kinetics. Ann. Rev. Phys. Chem. 58, 35–55 (2007)

    Article  Google Scholar 

  4. Gillespie, D.: A rigorous derivation of the chemical master equation. Physica A 188, 404–425 (1992)

    Article  Google Scholar 

  5. Gillespie, D.: Markov Processes: An Introduction for Physical Scientists. Academic (1992)

    Google Scholar 

  6. Gillespie, D.: The mathematics of Brownian motion and Johnson noise. Am. J. Phys. 64, 225–240 (1996)

    Article  MathSciNet  Google Scholar 

  7. Gillespie, D.: The multivariate Langevin and Fokker-Planck equations. Am. J. Phys. 64, 1246–1257 (1996)

    Article  MathSciNet  Google Scholar 

  8. Gillespie, D.: The chemical Langevin equation. J. Chem. Phys. 113, 297–306 (2000)

    Article  Google Scholar 

  9. Gillespie, D.: The chemical Langevin and Fokker-Planck equations for the reversible isomerization reaction. J. Phys. Chem. A 106, 5063–5071 (2002)

    Article  Google Scholar 

  10. Lok, L., Brent, R.: Automatic generation of cellular reaction networks with Moleculizer 1. 0. Nature Biotechnology 23, 131–136 (2005)

    Article  Google Scholar 

  11. Gillespie, D.: Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115, 1716–1733 (2001)

    Article  Google Scholar 

  12. Gillespie, D., Petzold, L.: Improved leap-size selection for accelerated stochastic simulation. J. Chem. Phys. 119, 8229–8234 (2003)

    Article  Google Scholar 

  13. Cao, Y., Gillespie, D., Petzold, L.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124, 044109 (2006)

    Article  Google Scholar 

  14. Cao, Y., Gillespie, D., Petzold, L.: Avoiding negative populations in explicit Poisson tau-leaping. J. Chem. Phys. 123, 054104 (2005)

    Article  Google Scholar 

  15. Rathinam, M., Petzold, L., Cao, Y., Gillespie, D.: Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method. J. Chem. Phys. 119, 12784–12794 (2003)

    Article  Google Scholar 

  16. Rathinam, M., Petzold, L., Cao, Y., Gillespie, D.: Consistency and stability of tau-leaping schemes for chemical reaction systems. Multiscale Model. Simul. 4, 867–895 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  17. Ascher, M., Petzold, L.: Computer Methods for Ordinary Differential Equations and Differential Algebraic Equations. In: SIAM (1998)

    Google Scholar 

  18. Cao, Y., Gillespie, D., Petzold, L.: The slow-scale stochastic simulation algorithm. J. Chem. Phys. 122, 014116 (2005)

    Article  Google Scholar 

  19. Cao, Y., Gillespie, D., Petzold, L.: Accelerated stochastic simulation of the stiff enzyme-substrate reaction. J. Chem. Phys. 123, 144917 (2005)

    Article  Google Scholar 

  20. Gillespie, D., Petzold, L., Cao, Y.: Comment on Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates. (J. Chem. Phys. 123, 0194107 (2005)); J. Chem. Phys. 126, 0137101 (2007)

    Google Scholar 

  21. Liu, E.W., Vanden-Eijnden, D., Nested, E.: stochastic simulation algorithm for chemical kinetic systems with disparate rates. J. Chem. Phys. 123, 194107 (2005)

    Article  Google Scholar 

  22. Cao, Y., Gillespie, D., Petzold, L.: Adaptive explicit-implicit tau-leaping with automatic tau-selection. J. Chem. Phys. 126, 224101 (2007)

    Article  Google Scholar 

  23. Gillespie, D., Lampoudi, S., Petzold, L.: Effect of reactant size on discrete stochastic chemical kinetics. J. Chem. Phys. 126, 034302 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Marco Bernardo Pierpaolo Degano Gianluigi Zavattaro

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gillespie, D.T. (2008). Simulation Methods in Systems Biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds) Formal Methods for Computational Systems Biology. SFM 2008. Lecture Notes in Computer Science, vol 5016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68894-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-68894-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68892-1

  • Online ISBN: 978-3-540-68894-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics