RKappa: Statistical Sampling Suite for Kappa Models

  • Anatoly SorokinEmail author
  • Oksana Sorokina
  • J. Douglas Armstrong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7699)


We present RKappa, a framework for the development and analysis of rule-based models within a mature, statistically empowered R environment. The infrastructure allows model editing, modification, parameter sampling, simulation, statistical analysis and visualisation without leaving the R environment. We demonstrate its effectiveness through its application to Global Sensitivity Analysis, exploring it in “parallel” and “concurrent” implementations.

The pipeline was designed for high performance computing platforms and aims to facilitate analysis of the behaviour of large-scale systems with limited knowledge of exact mechanisms and respectively sparse availability of parameter values. We illustrate it here with two biological examples. The package is available on github:


Global Sensitivity Analysis Rule-based modeling Model composition Model analysis 



AS was partially supported by RFBR, research project No. 14-44-03679 r_centr_a, and European Research Council (ERC) under grants 320823 RULE. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement nos. 241498 (EUROSPIN project), 242167 (SynSys-project) and 604102 (Human Brain Project). This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) ( The ECDF is partially supported by the eDIKT initiative (


  1. 1.
    Chylek, L.A., Harris, L.A., Tung, C.-S., Faeder, J.R., Lopez, C.F., Hlavacek, W.S.: Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdisc. Rev. Syst. Biol. Med. 6, 13–36 (2014)CrossRefGoogle Scholar
  2. 2.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling and model perturbation. In: Priami, C., Back, R.-J., Petre, I. (eds.) Transactions on Computational Systems Biology XI. LNCS, vol. 5750, pp. 116–137. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  3. 3.
    Faeder, J.R., Blinov, M.L., Hlavacek, W.S.: Rule-based modeling of biochemical systems with BioNetGen. Syst. Biol. Methods Mol. Biol. 500, 113–167 (2009)CrossRefGoogle Scholar
  4. 4.
    Novere, N.L., Shimizu, T.S.: STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics 17, 575–576 (2001)CrossRefGoogle Scholar
  5. 5.
    Danos, V., Feret, J., Fontana, W., Krivine, J.: Scalable simulation of cellular signaling networks. In: Shao, Z. (ed.) APLAS 2007. LNCS, vol. 4807, pp. 139–157. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  6. 6.
    Sorokin, A., Temlyakova, E.: Rule-based model of bacterial transcription initiation. FEBS J. 280, 569 (2013)Google Scholar
  7. 7.
    Sorokina, O., Sorokin, A., Armstrong, J.D.: Towards a quantitative model of the post-synaptic proteome. Mol. BioSyst. 7, 2813–2823 (2011)CrossRefGoogle Scholar
  8. 8.
    Marino, S., Hogue, I.B., Ray, C.J., Kirschner, D.E.: A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Maiwald, T., Timmer, J.: Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24, 2037–2043 (2008)CrossRefGoogle Scholar
  10. 10.
    Mendes, P., Hoops, S., Sahle, S., Gauges, R., Dada, J., Kummer, U.: Computational modeling of biochemical networks using COPASI. Syst. Biol. Methods Mol. Biol. 500, 17–59 (2009)CrossRefGoogle Scholar
  11. 11.
    Schmidt, H., Jirstrand, M.: Systems biology toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics 22, 514–515 (2006)CrossRefGoogle Scholar
  12. 12.
    Zi, Z., Zheng, Y., Rundell, A.E., Klipp, E.: SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool. BMC Bioinf. 9, 342 (2008)CrossRefGoogle Scholar
  13. 13.
    Zi, Z.: SBML-PET-MPI: a parallel parameter estimation tool for systems biology markup language based models. Bioinformatics 27, 1028–1029 (2011)CrossRefGoogle Scholar
  14. 14.
    Sneddon, M.W., Faeder, J.R., Emonet, T.: Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nature Methods 8, 177–183 (2010)CrossRefGoogle Scholar
  15. 15.
    Cho, K.-H., Shin, S.-Y., Kolch, W., Wolkenhauer, O.: Experimental design in systems biology, based on parameter sensitivity analysis using a monte carlo method: a case study for the TNF?-mediated NF-? B Sign. Transduct. Pathway Simul. 79, 726–739 (2003)Google Scholar
  16. 16.
    The Kappa Language.
  17. 17.
    igraph: The network analysis package.
  18. 18.
    Pujol, G., Iooss, B.: Sensitivity: Sensitivity Analysis in R (2008)Google Scholar
  19. 19.
    Baron, M.K., Boeckers, T.M., Vaida, B., Faham, S., Gingery, M., Sawaya, M.R., Salyer, D., Gundelfinger, E.D., Bowie, J.U.: An architectural framework that may lie at the core of the postsynaptic density. Science 311, 531–535 (2006)CrossRefGoogle Scholar
  20. 20.
    Cheng, D., Hoogenraad, C.C., Rush, J., Ramm, E., Schlager, M.A., Duong, D.M., Xu, P., Wijayawardana, S.R., Hanfelt, J., Nakagawa, T., Sheng, M., Peng, J.: Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum. Mol. Cell. Proteomics 5, 1158–1170 (2006)CrossRefGoogle Scholar
  21. 21.
    Nourry, C., Grant, S.G.N., Borg, J.-P.: PDZ Domain Proteins: Plug and Play! Sci. STKE 179, re7 (2003)Google Scholar
  22. 22.
    Carlisle, H.J., Fink, A.E., Grant, S.G.N., O’Dell, T.J.: Opposing effects of PSD-93 and PSD-95 on long-term potentiation and spike timing-dependent plasticity. J. Physiol. 586, 5885–5900 (2008)CrossRefGoogle Scholar
  23. 23.
    Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27, 55–71 (2005)CrossRefGoogle Scholar
  24. 24.
    Saecker Jr, R.M., M.T.R., deHaseth, P.L.,: Mechanism of bacterial transcription initiation: RNA polymerase - promoter binding, isomerization to initiation-competent open complexes, and initiation of RNA synthesis. J. MolecularBiology 412, 754–771 (2011)Google Scholar
  25. 25.
    Liang, S.-T., Bipatnath, M., Xu, Y.-C., Chen, S.-L., Dennis, P., Ehrenberg, M., Bremer, H.: Activities of constitutive promoters in Escherichia coli. J. Mol. Biol. 2921, 19–37 (1999)CrossRefGoogle Scholar
  26. 26.
    Ishihama, A.: Functional modulation of escherichia coli rna polymerase. Microbiology 54, 499–518 (2000)CrossRefGoogle Scholar
  27. 27.
    Ishihama, Y., Schmidt, T., Rappsilber, J., Mann, M., Hartl, F.U., Kerner, M.J., Frishman, D.: Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 9, 102 (2008)CrossRefGoogle Scholar
  28. 28.
    Sclavi, B., Zaychikov, E., Rogozina, A., Walther, F., Buckle, M., Heumann, H.: Real-time characterization of intermediates in the pathway to open complex formation by Escherichia coli RNA polymerase at the T7A1 promoter. PNAS 102, 4706–4711 (2005)CrossRefGoogle Scholar
  29. 29.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. 69, 026113 (2004)Google Scholar
  30. 30.
    Wang, J., Li, M., Deng, Y., Pan, Y.: Recent advances in clustering methods for protein interaction networks. BMC Genomics 11, S10 (2010)Google Scholar
  31. 31.
    Pocklington, A.J., Cumiskey, M., Armstrong, J.D., Grant, S.G.N.: The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour. Mol Syst Biol. 2, 2006.0023 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anatoly Sorokin
    • 1
    • 2
    Email author
  • Oksana Sorokina
    • 2
  • J. Douglas Armstrong
    • 2
  1. 1.Institute of Cell Biophysics RASPushchinoRussia
  2. 2.The University of EdinburghEdinburghUK

Personalised recommendations