A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models.

ABSTRACT: 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 transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for

SUBMITTER: Romers J 

PROVIDER: S-EPMC6954118 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature