Using rxncon to Develop Rule-Based Models
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 wordsSignal transduction rxncon Network reconstruction Rule-based modeling Boolean/logical modeling
This work was supported by the German Federal Ministry of Education and Research via e:Bio Cellemental (FKZ0316193, to MK).
- 2.Danos V, Feret J, Fontana W, Harmer R, Krivine J (2007) Rule-based modelling of cellular signalling. In: Caires L, Vasconcelos VT (eds) CONCUR 2007 – Concurrency Theory: 18th International Conference, CONCUR 2007, Lisbon, Portugal, September 3–8, 2007. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 17–41. https://doi.org/10.1007/978-3-540-74407-8_3 CrossRefGoogle Scholar
- 9.Romers JC, Krantz M (2017) Pre-print: rxncon 2.0: a language for executable molecular systems biology. bioRxiv. https://doi.org/10.1101/107136
- 11.Lubitz T, Welkenhuysen N, Shashkova S, Bendrioua L, Hohmann S, Klipp E, Krantz M (2015) Network reconstruction and validation of the Snf1/AMPK pathway in baker’s yeast based on a comprehensive literature review. NPJ Syst Biol Appl 1:15007. https://doi.org/10.1038/npjsba.2015.7 http://www.nature.com/articles/npjsba20157#supplementary-information CrossRefPubMedPubMedCentralGoogle Scholar
- 12.Romers JC, Thieme S, Münzner U, Krantz M (2017) Pre-print: a scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. bioRxiv. https://doi.org/10.1101/107235