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Open Access Methodology article

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

Matthew S Creamer12, Edward C Stites1, Meraj Aziz1, James A Cahill2, Chin Wee Tan3, Michael E Berens4, Haiyong Han1, Kimberley J Bussey1, Daniel D Von Hoff1, William S Hlavacek156 and Richard G Posner12*

Author affiliations

1 Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA

2 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA

3 Ludwig Institute for Cancer Research, Melbourne-Parkville Branch, Royal Melbourne Hospital, Parkville, Victoria, 3050, Australia

4 Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA

5 Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

6 Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA

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Citation and License

BMC Systems Biology 2012, 6:107  doi:10.1186/1752-0509-6-107

Published: 22 August 2012

Abstract

Background

Mathematical/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.

Results

Here, 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.

Conclusions

With 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.

Keywords:
Systems biology; Epidermal growth factor (EGF) receptor (EGFR); Rule-based modeling; Temporal phosphoproteomics