  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.
