Advertisement

RKappa: Software for Analyzing Rule-Based Models

  • Anatoly Sorokin
  • Oksana Sorokina
  • J. Douglas Armstrong
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

Abstract

RKappa is a framework for the development, simulation, and analysis of rule-based models within the mature statistically empowered R environment. It is designed for model editing, parameter identification, simulation, sensitivity analysis, and visualization. The framework is optimized for high-performance computing platforms and facilitates analysis of large-scale systems biology models where knowledge of exact mechanisms is limited and parameter values are uncertain.

The RKappa software is an open-source (GLP3 license) package for R, which is freely available online (https://github.com/lptolik/R4Kappa).

Key words

RKappa Rule-based modeling Exploratory analysis Global sensitivity analysis 

References

  1. 1.
    Danos V, Laneve C (2004) Formal molecular biology. Theor Comput Sci 325:69–110CrossRefGoogle Scholar
  2. 2.
    Blinov ML, Faeder JR, Goldstein B, Hlavacek WS (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289–3291CrossRefGoogle Scholar
  3. 3.
    Le Novère N, Shimizu TS (2001) STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics 17:575–576CrossRefGoogle Scholar
  4. 4.
    Chylek LA, Harris LA, Tung CS et al (2014) Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdiscip Rev Syst Biol Med 6:13–36CrossRefGoogle Scholar
  5. 5.
    Sorokina O, Sorokin A, Armstrong JD (2013) A simulator for spatially extended Kappa models. Bioinformatics 29:3105–3106CrossRefGoogle Scholar
  6. 6.
    Grünert G, Dittrich P (2011) Using the SRSim software for spatial and rule-based modeling of combinatorially complex biochemical reaction systems. In: Gheorghe M, Hinze T, Păun G, Rozenberg G, Salomaa A (eds) Membrane computing, vol 6501. Springer, Berlin., Lect Notes Comput Sci, pp 240–256CrossRefGoogle Scholar
  7. 7.
    Plimpton S, Slepoy A (2005) Microbial cell modeling via reacting diffusive particles. J Phys Conf Ser 16:305–309CrossRefGoogle Scholar
  8. 8.
    Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1:137–151CrossRefGoogle Scholar
  9. 9.
    Stiles J, Bartol T (2001) Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: Erik DS (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, FL, pp 87–127Google Scholar
  10. 10.
    Sneddon MW, Faeder JR, Emonet T (2010) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8:177–183CrossRefGoogle Scholar
  11. 11.
    Thomas BR, Chylek LA, Colvin J et al (2016) BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments. Bioinformatics 32:798–800CrossRefGoogle Scholar
  12. 12.
    Marino S, Hogue IB, Ray CJ, Kirschner DE (2008) A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol 254:178–196CrossRefGoogle Scholar
  13. 13.
    Lebedeva G, Sorokin A, Faratian D et al (2012) Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network. Eur J Pharm Sci 46:244–258CrossRefGoogle Scholar
  14. 14.
    Sorokin A, Sorokina O, Armstrong JD (2015) RKappa: statistical sampling suite for Kappa models. In: Maler O, Halász Á, Dang T, Piazza C (eds) Hybrid systems biology, vol 7699. Springer, Cham., Lect Notes Comput Sci, pp 128–142CrossRefGoogle Scholar
  15. 15.
    Pujol G, Iooss B, Janon A (2015) sensitivity package, version 1.11. The comprehensive R Archive Network, http://www.cran.r-project.org/web/packages/sensitivity. Accessed 26 Aug 2016
  16. 16.
    Sorokina O, Sorokin A, Armstrong JD (2011) Towards a quantitative model of the post-synaptic proteome. Mol BioSyst 7:2813–2823CrossRefGoogle Scholar
  17. 17.
    Cho KH, Shin SY, Kolch W et al (2003) Experimental design in systems biology, based on parameter sensitivity analysis using a Monte Carlo method: a case study for the TNFα-mediated NFκB signal transduction pathway. SIMULATION 79:726–739CrossRefGoogle Scholar
  18. 18.
    Roustant O, Ginsbourger D, Deville Y (2012) DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J Stat Softw 51(1):1–55.  https://doi.org/10.18637/jss.v051.i01 CrossRefGoogle Scholar
  19. 19.
    Bischl B, Lang M et al (2015) BatchExperiments: abstraction mechanisms for using Rin batch environments. J Stat Softw 64(11):1–25CrossRefGoogle Scholar
  20. 20.
    Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40:1–29Google Scholar
  21. 21.
    Wang D, Murphy M (2005) Identifying nonlinear relationships in regression using the ACE Algorithm. J Appl Stat 32:243–258CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Anatoly Sorokin
    • 1
    • 2
  • Oksana Sorokina
    • 3
  • J. Douglas Armstrong
    • 3
  1. 1.Institute of Cell BiophysicsRussian Academy of SciencesMoscow RegionRussia
  2. 2.Moscow Institute of Physics and TechnologyMoscow RegionRussia
  3. 3.School of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations