Skip to main content

Kappa Rule-Based Modeling in Synthetic Biology

  • Protocol
  • First Online:
Computational Methods in Synthetic Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1244))

Abstract

Rule-based modeling, an alternative to traditional reaction-based modeling, allows us to intuitively specify biological interactions while abstracting from the underlying combinatorial complexity. One such rule-based modeling formalism is Kappa, which we introduce to readers in this chapter. We discuss the application of Kappa to three modeling scenarios in synthetic biology: a unidirectional switch based on nitrosylase induction in Saccharomyces cerevisiae, the repressilator in Escherichia coli formed from BioBrick parts, and a light-mediated extension to said repressilator developed by the University of Edinburgh team during iGEM 2010. The second and third scenarios in particular form a case-based introduction to the Kappa BioBrick Framework, allowing us to systematically address the modeling of devices and circuits based on BioBrick parts in Kappa. Through the use of these examples, we highlight the ease with which Kappa can model biological interactions both at the genetic and the protein–protein interaction level, resulting in detailed stochastic models accounting naturally for transcriptional and translational resource usage. We also hope to impart the intuitively modular nature of the modeling processes involved, supported by the introduction of visual representations of Kappa models. Concluding, we explore future endeavors aimed at making modeling of synthetic biology more user-friendly and accessible, taking advantage of the strengths of rule-based modeling in Kappa.

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

Access this chapter

eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and 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
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Danos V, Laneve C (2004) Formal molecular biology. Theor Comput Sci 325:69–110

    Article  Google Scholar 

  2. Blinov ML, Faeder JR, Goldstein B et al (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289–3291

    Article  CAS  PubMed  Google Scholar 

  3. Bachman JA, Sorger P (2011) New approaches to modeling complex biochemistry. Nat Methods 8:130

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361

    Article  CAS  Google Scholar 

  5. Feret J, Krivine J (2013) KaSim3 reference manual. https://github.com/jkrivine/KaSim/blob/master/man/KaSim_manual.pdf?raw=true. Accessed 26 June 2013

  6. Danos V, Honorato-Zimmer R, Stucki S (2013) KaSpace: a language for the combinatorial assembly of biological complexes. The First Annual Winter q-bio Meeting

    Google Scholar 

  7. Pedersen M, Phillips A, Plotkin G (2013) A high-level language for rule-based modelling. http://mdpedersen.azurewebsites.net/papers/lbs-kappa.pdf. Accessed 15 September 2014

  8. Goldbeter A, Koshland DE (1981) An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci U S A 78:6840–6844

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Wang D, Amornsiripanitch N, Dong X (2006) A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants. PLoS Pathog 2:e123

    Article  PubMed Central  PubMed  Google Scholar 

  10. Moore JW (2012) Foundation technologies in synthetic biology: tools for use in understanding plant immunity. PhD thesis, University of Edinburgh

    Google Scholar 

  11. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403:339–342

    Article  CAS  PubMed  Google Scholar 

  12. Shetty RP, Endy D, Knight TF Jr (2008) Engineering BioBrick vectors from BioBrick parts. J Biol Eng 2:1–12

    Article  Google Scholar 

  13. Marchisio MA, Stelling J (2008) Computational design of synthetic gene circuits with composable parts. Bioinformatics 24(17):1903–1910

    Article  CAS  PubMed  Google Scholar 

  14. Chandran D, Bergmann FT, Sauro HM (2009) TinkerCell: modular CAD tool for synthetic biology. J Biol Eng 3:19

    Article  PubMed Central  PubMed  Google Scholar 

  15. Marchisio MA, Colaiacovo M, Whitehead E et al (2013) Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits. BMC Syst Biol 7:42

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403:335–338

    Article  CAS  PubMed  Google Scholar 

  17. Stewart D, Wilson-Kanamori JR (2011) Modular modelling in synthetic biology: light-based communication in E. coli. Electron Notes Theor Comput Sci 277:77–87

    Article  Google Scholar 

  18. Cox RS, Surette MG, Elowitz MB (2007) Programming gene expression with combinatorial promoters. Mol Syst Biol 3

    Google Scholar 

  19. Danos V, Feret J, Fontana W et al (2009) Rule-based modelling and model perturbation. Lect Notes Comput Sci 5750:116–137

    Article  Google Scholar 

  20. Danos V, Harmer R, Honorato-Zimmer R (2013) Thermodynamic graph-rewriting. Lect Notes Comput Sci 8052:380–394

    Article  Google Scholar 

  21. Metropolis N, Rosenbluth AW, Rosenbluth MN et al (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087

    Article  CAS  Google Scholar 

  22. Karr JR, Sanghvi JC, Macklin DN et al (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John Wilson-Kanamori .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Wilson-Kanamori, J., Danos, V., Thomson, T., Honorato-Zimmer, R. (2015). Kappa Rule-Based Modeling in Synthetic Biology. In: Marchisio, M. (eds) Computational Methods in Synthetic Biology. Methods in Molecular Biology, vol 1244. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1878-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1878-2_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1877-5

  • Online ISBN: 978-1-4939-1878-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics