Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

Matthew S Creamer,William S Hlavacek,Chin Wee Tan,Richard G Posner,Kimberley J Bussey,Edward C Stites,Haiyong Han,Meraj Aziz,James A Cahill,Daniel D Von Hoff,Michael E Berens

doi:10.1186/1752-0509-6-107

Abstract

BackgroundMathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions.ResultsHere, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map.ConclusionsWith the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.

Highlights

Mathematical/computational models are needed to understand cell signaling networks, which are complex
We apply the conventions of Chylek et al [22] to visualize and annotate our model, and we demonstrate that the model can be simulated using recently developed software implementing a network-free simulation approach that enables the simulation of interactions marked by combinatorial complexity [13]
We specified a rule-based model for molecular interactions in the ERBB receptor signaling network

Summary

Introduction

Mathematical/computational models are needed to understand cell signaling networks, which are complex. The number of chemical species that can be populated in a cell signaling network, and the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. Models capturing mass-action chemical kinetics can be specified in various traditional forms, such as that of ordinary differential equations (ODEs). This approach has been quite useful for studying small modules at biochemical reaction resolution [4]. Coarser resolution models of larger networks have been useful for studying systemic properties, for example, how processes such as feedback and internalization may influence receptor tyrosine kinase signaling [5,6]

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Conclusion