The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not yet been established for signal transduction networks, which appear to be much more challenging targets. This is due to the internal states carried by the signalling components, which encode and transmit the information through the network. Dealing with these states leads to scalability problems at two distinct level: First, in the model formulation, and second, in the model execution. For large-scale signalling networks, rule based modelling has been established as the quantitative method of choice due efficient model definition (as rules) and execution (as agents). However, rule based models cannot be simulated without quantitative parameters, introducing yet another layer of uncertainty. Consequently, it would be advantageous if models of signal transduction could be simulated and validated at the qualitative level. Here, we present a method for qualitative analysis of large-scale signalling networks. It is based on rxncon, the reaction-contingency language, a language for reconstruction of signal transduction networks. We developed a method to generate generic update rules for both the states and the reactions, the elementary building blocks in such a network, which can be used to map an arbitrary rxncon system on a unique bipartite Boolean model with a defined set of truth tables. Hence, each rxncon network can be converted into a corresponding executable model, providing a powerful tool for network validation. Furthermore, the rxncon network can also be compiled into a rule based model. Taken together, we equip rxncon 2.0 with a qualitative simulation tool, which can be used in network validation and model development.