Domain-Oriented Reduction of Rule-Based Network Models

Authors

Nikolay M. Borisov, Thomas Jefferson UniversityFollow
Alexander S. Chistopolsky, Burnasyan Federal Medical Biophysical Center
James R. Faeder, University of Pittsburgh School of Medicine
Boris N. Kholodenko, Thomas Jefferson UniversityFollow

Document Type

Article

Publication Date

September 2008

Comments

This article has been peer reviewed. It is the authors' final version prior to publication in IET Systems Biology 2(5):342-351, 2008. The published version is available at DOI: 10.1049/iet-syb:20070081. Copyright and copy; Institution of Engineering and Technology.

Abstract

The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signaling network. We have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each “full” (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. We describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modeling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model; and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. We tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signaling networks, and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerization, and loss of accuracy when dynamics are computed stochastically.

Recommended Citation

Borisov, Nikolay M.; Chistopolsky, Alexander S.; Faeder, James R.; and Kholodenko, Boris N., "Domain-Oriented Reduction of Rule-Based Network Models" (2008). Department of Pathology, Anatomy, and Cell Biology Faculty Papers. Paper 23.
https://jdc.jefferson.edu/pacbfp/23