Skip to main content
SpringerLink
Log in

From Quantitative SBML Models to Boolean Networks

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1073))

Included in the following conference series:

Abstract

Modelling complex biological systems is necessary for their study and understanding. SBML is the standard format to represent models of biological systems. Most of the curated models available in the repository Biomodels are quantitative, but in some cases qualitative models—such as Boolean networks—would be better suited. This paper is the first to focus on the automatic transformation of quantitative SBML models to Boolean networks. We propose SBML2BN, a pipeline dedicated to this task. By running SBML2BN on more than 200 quantitative SBML models, we provide evidence that we can automatically construct Boolean networks which are compatible with the structure and the dynamics of a given quantitative SBML model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
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

Notes

  1. 1.

    http://sbml.org/Documents/Specifications.

  2. 2.

    Release 31 ftp://ftp.ebi.ac.uk/pub/databases/biomodels/releases/2017-06-26/.

  3. 3.

    https://www.ebi.ac.uk/biomodels/BIOMD0000000044.

References

  1. Conda: Anaconda Software Distribution (2021)

    Google Scholar 

  2. Aghamiri, S.S., Singh, V., Naldi, A., Helikar, T., Soliman, S., Niarakis, A.: Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics 36(16), 4473–4482 (2020)

    Article  Google Scholar 

  3. Biane, C., Delaplace, F., Melliti, T.: Abductive network action inference for targeted therapy discovery. Electron. Notes Theor. Comput. Sci. 335, 3–25 (2018)

    Article  MATH  Google Scholar 

  4. Bornstein, B.J., Keating, S.M., Jouraku, A., Hucka, M.: LibSBML: an API library for SBML. Bioinformatics 24(6), 880–881 (2008)

    Article  Google Scholar 

  5. Chatain, T., Haar, S., Kolčák, J., Paulevé, L.: Most Permissive Semantics of Boolean Networks. In: Research Report, University Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, F-33400 Talence, France; LSV, ENS Cachan, CNRS, INRIA, Université Paris-Saclay, Cachan (France) (2020)

    Google Scholar 

  6. Chevalier, S., Noël, V., Calzone, L., Zinovyev, A., Paulevé, L.: Synthesis and simulation of ensembles of Boolean networks for cell fate decision. In: Abate, A., Petrov, T., Wolf, V. (eds.) CMSB 2020. LNCS, vol. 12314, pp. 193–209. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60327-4_11

    Chapter  Google Scholar 

  7. Courtot, M., et al.: Controlled vocabularies and semantics in systems biology. Molecular Syst. Bio. 7(1), 543 (2011)

    Article  Google Scholar 

  8. Davidich, M., Bornholdt, S.: The transition from differential equations to boolean networks: a case study in simplifying a regulatory network model. J. Theor. Bio. 255(3), 269–277 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Fages, F., Gay, S., Soliman, S.: Automatic Curation of SBML Models based on their ODE Semantics. Research Report RR-8014, INRIA (2012)

    Google Scholar 

  10. Fages, F., Soliman, S.: Abstract interpretation and types for systems biology. Theoret. Comput. Sci. 403(1), 52–70 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Fages, F., Soliman, S.: From reaction models to influence graphs and back: a theorem. In: Fisher, J. (ed.) FMSB 2008. LNCS, vol. 5054, pp. 90–102. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68413-8_7

    Chapter  MATH  Google Scholar 

  12. Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Morgan and Claypool Publishers, New York (2012)

    MATH  Google Scholar 

  13. Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.: COPASI—a complex pathway simulator. Bioinformatics 22(24), 3067–3074 (2006)

    Article  Google Scholar 

  14. Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Bio. 22(3), 437–467 (1969)

    Article  MathSciNet  Google Scholar 

  15. Keating, S.M., et al.: SBML Level 3 Community members: SBML Level 3: An extensible format for the exchange and reuse of biological models. Molecular Syst. Bio. 16(8), e9110 (2020)

    Google Scholar 

  16. Klarner, H., Heinitz, F., Nee, S., Siebert, H.: Basins of attraction, commitment sets, and phenotypes of boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(4), 1115–1124 (2020)

    Google Scholar 

  17. Klarner, H., Streck, A., Siebert, H.: PyBoolNet: a python package for the generation, analysis and visualization of boolean networks. Bioinformatics 33(5), 770–772 (2016)

    Article  Google Scholar 

  18. Lähdesmäki, H., Shmulevich, I., Yli-Harja, O.: On learning gene regulatory networks under the boolean network model. Mach. Learn. 52(1), 147–167 (2003)

    Article  MATH  Google Scholar 

  19. Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Pacific Symposium on Biocomputing, pp. 18–29 (1998)

    Google Scholar 

  20. Malik-Sheriff, R.S., et al.: BioModels–15 years of sharing computational models in life science. Nucleic Acids Res. 48(D1), D407–D415 (2020)

    Google Scholar 

  21. Mölder, F., et al.: Sustainable data analysis with Snakemake. F1000Research 10, 33 (2021)

    Google Scholar 

  22. Ostrowski, M., Paulevé, L., Schaub, T., Siegel, A., Guziolowski, C.: Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming. Biosystems 149, 139–153 (2016)

    Article  Google Scholar 

  23. Paulevé, L., Kolčák, J., Chatain, T., Haar, S.: Reconciling qualitative, abstract, and scalable modeling of biological networks. Nat. Commun. 11(1), 4256 (2020)

    Article  Google Scholar 

  24. Schwab, J.D., Kühlwein, S.D., Ikonomi, N., Kühl, M., Kestler, H.A.: Concepts in Boolean network modeling: what do they all mean? Comput. Struct. Biotechnol. J. 18, 571–582 (2020)

    Article  Google Scholar 

  25. Thomas, R.: Boolean formalization of genetic control circuits. J. Theor. Biol. 42(3), 563–585 (1973)

    Article  Google Scholar 

  26. Vaginay, A., Boukhobza, T., Smaïl-Tabbone, M.: Automatic Synthesis of Boolean Networks from Biological Knowledge and Data. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds.) OLA 2021. CCIS, vol. 1443, pp. 156–170. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85672-4_12

    Chapter  Google Scholar 

Download references

Acknowledgements

We thank Hans-Jörg Schurr for his valuable comments and suggestions and Laurine Hubert for helpful comments on an early draft.

Author information

Authors and Affiliations

  1. Université de Lorraine, CNRS, CRAN, 54000, Nancy, France

    Athénaïs Vaginay & Taha Boukhobza

  2. Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France

    Athénaïs Vaginay & Malika Smaïl-Tabbone

Authors
  1. Athénaïs Vaginay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taha Boukhobza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malika Smaïl-Tabbone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athénaïs Vaginay .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Compl, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Complex Networks and Systems Research School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vaginay, A., Boukhobza, T., Smaïl-Tabbone, M. (2022). From Quantitative SBML Models to Boolean Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93413-2_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93412-5

  • Online ISBN: 978-3-030-93413-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation