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Using rxncon to Develop Rule-Based Models

  • Jesper Romers
  • Sebastian Thieme
  • Ulrike Münzner
  • Marcus KrantzEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

Abstract

We present a protocol for building, validating, and simulating models of signal transduction networks. These networks are challenging modeling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalize the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalize large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule-based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signaling pathway to illustrate the protocol, together with some of the challenges—and some of their solutions—in modeling signal transduction.

Key words

Signal transduction rxncon Network reconstruction Rule-based modeling Boolean/logical modeling 

Notes

Acknowledgments

This work was supported by the German Federal Ministry of Education and Research via e:Bio Cellemental (FKZ0316193, to MK).

Supplementary material

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Copyright information

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

Authors and Affiliations

  • Jesper Romers
    • 1
  • Sebastian Thieme
    • 1
  • Ulrike Münzner
    • 1
    • 2
  • Marcus Krantz
    • 1
    Email author
  1. 1.Institute of BiologyHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan

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