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 been established for signal transductionnetworks, where the representation of complexes and internal states leads to scalability issues in both model formulation andexecution. While rule- and agent-based methods allow efficient model definition and execution, respectively, modelparametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we presenta scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses abipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation ofany rxncon model into a unique Boolean model, which can be used for network validation and simulation—allowing the predictionof system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and theindependence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signaltransduction networks.
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