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Cited by 4Gene Regulatory Network AnalysisSimulation Techniques and ApplicationsMicrobial Metabolic Engineering and Bioproduction

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RKappa: Software for Analyzing Rule-Based Models

Sorokin, Anatoly, Sorokina, Oksana, Douglas Armstrong, J.·2019·Methods in Molecular Biology
DOI 10.1007/978-1-4939-9102-0_17
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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 ).

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Crossref · 10.1007/978-1-4939-9102-0_17

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2 of 2 references verified0 citation errors · 0 not checkable

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  • The reason for the few rule-based models might be partly because support for thorough experimentation with rule-based models including calibration and validation has only become available during the last decade [97-99].

    “The reason for this might be partly because support for thorough experimentation with rule-based models including calibration and validation has only become available during the last decade [97–99].”

    From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [99]

Citations

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Research works printed but not linked: 18

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  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 20:3289–3291 · DOI
  3. Le Novère N, Shimizu TS (2001) STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics 17:575–576 · DOI
  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 6:13–36 · DOI
  5. Sorokina O, Sorokin A, Armstrong JD (2013) A simulator for spatially extended Kappa models. Bioinformatics 29:3105–3106 · DOI
  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 240–256 · DOI
  7. Plimpton S, Slepoy A (2005) Microbial cell modeling via reacting diffusive particles. J Phys Conf Ser 16:305–309 · DOI
  8. Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1:137–151 · DOI
  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 87–127
  10. Sneddon MW, Faeder JR, Emonet T (2010) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8:177–183 · DOI
  11. Marino S, Hogue IB, Ray CJ, Kirschner DE (2008) A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol 254:178–196 · DOI · Paper page
  12. 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 46:244–258 · DOI
  13. 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 128–142 · DOI
  14. Sorokina O, Sorokin A, Armstrong JD (2011) Towards a quantitative model of the post-synaptic proteome. Mol BioSyst 7:2813–2823 · DOI
  15. 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 · DOI
  16. Bischl B, Lang M et al (2015) BatchExperiments: abstraction mechanisms for using Rin batch environments. J Stat Softw 64(11):1–25 · DOI
  17. Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40:1–29
  18. Wang D, Murphy M (2005) Identifying nonlinear relationships in regression using the ACE Algorithm. J Appl Stat 32:243–258 · DOI

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Metadata

Authors
Sorokin, A., Sorokina, O., & Douglas Armstrong, J.
Affiliations
  • Russian Academy of Sciences (RU)
  • University of Edinburgh (GB)
ORCID
Venue
Methods in Molecular Biology
Publisher
Springer New York
Published
2019
Volume
1945
Pages
363-390
ISSN
  • 1064-3745 (ISSN-L)
  • 1940-6029
DOI
10.1007/978-1-4939-9102-0_17
OpenAlex
W2891801194
Cited by
4
References
2
Citations by year
2026: 1 · 2023: 1 · 2021: 1 · 2019: 1
FWCI
0.57
Type
book chapter
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • Methods in molecular biology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
rule-based models, systems biology modeling, R package, model simulation, sensitivity analysis, parameter estimation, model visualization, high-performance computing, uncertainty quantification
MeSH terms
Models, Biological, Software, Computational Biology, Systems Biology

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