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

Jesper Romers,Ulrike Münzner,Marcus Krantz,Sebastian Thieme

doi:10.1038/s41540-019-0120-5

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 reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation—allowing the prediction of system-level function directly from molecular mechanistic data. 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.

Highlights

Systems biology aims at the integrative analysis of large-scale biological systems up to whole cells
We present a parameter-free simulation method that supports large-scale mechanistic models of signal transduction networks. This bipartite Boolean modelling logic is based on the second generation rxncon language (Fig. 1), which is tailored for formalising signal transduction models based on empirical data:[16] the reaction network is defined in terms of elemental states, i.e. modifications at specific residues and bonds at specific domains
The starting point is an arbitrary model defined in the second generation rxncon language, and the end point a bipartite Boolean model prepared for simulation with the Boolnet package.[18]

Summary

INTRODUCTION

Systems biology aims at the integrative analysis of large-scale biological systems up to whole cells. We integrate knowledge into executable or computational models.[1] This process has been developed the furthest in the field of metabolic modelling, where the community routinely works with genome-scale models These models are defined at the level of biochemical reactions, cover the entire metabolic network of even complex cells, and can be simulated to predict system-level functionality.[2,3] The methodology is well established and supported by rich toolboxes for network reconstruction, validation and simulation,[4] and it constitutes the paradigm for bottom-up modelling. We present a parameter-free simulation method that supports large-scale mechanistic models of signal transduction networks This bipartite Boolean modelling (bBM) logic is based on the second generation rxncon language (Fig. 1), which is tailored for formalising signal transduction models based on empirical data:[16] the reaction network is defined in terms of elemental states, i.e. modifications (or lack thereof) at specific residues and bonds (or lack thereof) at specific domains. The definitions, where RHS and LHS refer to ling at the genome scale

RESULTS

DISCUSSION

METHODS