A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks.
Ontology highlight
ABSTRACT: Background
Receptors and scaffold proteins possess a number of distinct domains and bind multiple partners. A common problem in modeling signaling systems arises from a combinatorial explosion of different states generated by feasible molecular species. The number of possible species grows exponentially with the number of different docking sites and can easily reach several millions. Models accounting for this combinatorial variety become impractical for many applications.Results
Our results show that under realistic assumptions on domain interactions, the dynamics of signaling pathways can be exactly described by reduced, hierarchically structured models. The method presented here provides a rigorous way to model a large class of signaling networks using macro-states (macroscopic quantities such as the levels of occupancy of the binding domains) instead of micro-states (concentrations of individual species). The method is described using generic multidomain proteins and is applied to the molecule LAT.Conclusion
The presented method is a systematic and powerful tool to derive reduced model structures describing the dynamics of multiprotein complex formation accurately.
SUBMITTER: Conzelmann H
PROVIDER: S-EPMC1413560 | biostudies-literature | 2006 Jan
REPOSITORIES: biostudies-literature
Publications
A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks.
Conzelmann Holger H Saez-Rodriguez Julio J Sauter Thomas T Kholodenko Boris N BN Gilles Ernst D ED
BMC bioinformatics 20060123
<h4>Background</h4>Receptors and scaffold proteins possess a number of distinct domains and bind multiple partners. A common problem in modeling signaling systems arises from a combinatorial explosion of different states generated by feasible molecular species. The number of possible species grows exponentially with the number of different docking sites and can easily reach several millions. Models accounting for this combinatorial variety become impractical for many applications.<h4>Results</h4 ...[more]
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