The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. In contrast, current established methods do not support genome-scale mechanistic models of signal transduction networks. These networks encode information through internal states, and dealing with these states leads to scalability issues both in model formulation and execution. While rule based modelling can be used for efficient model definition (through rules) and simulation (through agent based execution), these quantitative models require parametrisation. This introduces yet another layer of uncertainty, due to the sparsity of reliably measured parameters. Hence, parameter-free simulation and validation will be important to support large-scale reconstruction and analysis of signal transduction network models. Here, we present a scalable method for parameterfree simulation of mechanistic signal transduction network models. It is based on rxncon, the reaction-contingency language, which describes the signalling network in terms of elemental reactions and states. We develop two generic update rules for states and reactions, based on detail analysis of two minimal reaction motifs, that can be used to map an arbitrary rxncon network on fully defined bipartite Boolean model. Locally defined update rules are assembled into a functional model without system level optimisation, making the methods suitable for network validation. Furthermore, an underlying model defined solely in terms of molecular reactions and causalities can be used to explain and predict system level behaviour. Taken together, we present a method for parameter-free simulation of mechanistic signal transduction models. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

10Understanding how cellular functions emerge from the underlying molecular mechanisms is a 11 key challenge in biology. This will require computational models, whose predictive power is 12 expected to increase with coverage and precision of formulation. Genome-scale models 13 revolutionised the metabolic field and made the first whole-cell model possible. However, the 14 lack of genome-scale models of signalling networks blocks the development of eukaryotic 15 whole-cell models. Here, we present a comprehensive mechanistic model of the molecular 16 network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the 17 reaction-contingency language, to neutralise the scalability issues preventing formulation, 18 visualisation and simulation of signalling networks at the genome-scale. We use parameter-19 free modelling to validate the network and to predict genotype-to-phenotype relationships 20 down to residue resolution. This mechanistic genome-scale model offers a new perspective on 21 eukaryotic cell cycle control, and opens up for similar models -and eventually whole-cell 22 models -of human cells. 23. CC-BY-NC 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.