PT  - JOURNAL ARTICLE
AU  - Jesper Romers
AU  - Sebastian Thieme
AU  - Ulrike Münzner
AU  - Marcus Krantz
TI  - A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models
AID  - 10.1101/107235
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 107235
4099  - http://biorxiv.org/content/early/2017/11/10/107235.short
4100  - http://biorxiv.org/content/early/2017/11/10/107235.full
AB  - 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 parameter-free 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.