Using rxncon to develop rule based models

TL;DR

This paper introduces a protocol using rxncon for building, validating, and simulating large, rule-based models of signal transduction networks, addressing complexity and data sparsity issues.

Contribution

It presents a comprehensive workflow for creating and refining large-scale signal transduction models using rxncon, including conversion to Boolean models and export to rule-based formats.

Findings

Successful modeling of insulin signaling pathway

Improved scalability and accuracy in network models

Workflow facilitates formalization of complex biological systems

Abstract

We present a protocol for building, validating and simulating models of signal transduction networks. These networks are challenging modelling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalise 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 formalise 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 signalling pathway to illustrate the protocol, together with some of the challenges - and some of their solutions - in modelling signal transduction.