DocumentCode :
951337
Title :
Domain-oriented reduction of rule-based network models
Author :
Borisov, N.M. ; Chistopolsky, A.S. ; Faeder, J.R. ; Kholodenko, Boris N.
Author_Institution :
Dept. of Pathology, Thomas Jefferson Univ., Philadelphia, PA
Volume :
2
Issue :
5
fYear :
2008
fDate :
9/1/2008 12:00:00 AM
Firstpage :
342
Lastpage :
351
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 signalling network. The authors 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 dasiafulldasia (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. The authors describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modelling 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. The authors tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signalling 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 heterodimerisation and loss of accuracy when dynamics are computed stochastically.
Keywords :
biology computing; biomembranes; proteins; stochastic processes; BioNetGen modelling package; auxiliary proteins; domain-oriented model reduction; domain-oriented reduction; heterodimerisation; immunoreceptor-mediated signalling networks; membrane-bound receptors; multidomain proteins; progenitor proteins; protein interactions; rule-based network models; stochastic process; transcriptional regulators;
fLanguage :
English
Journal_Title :
Systems Biology, IET
Publisher :
iet
ISSN :
1751-8849
Type :
jour
DOI :
10.1049/iet-syb:20070081
Filename :
4648915
Link To Document :
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