Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk.

TitleTopological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk.
Publication TypeJournal Article
Year of Publication2004
AuthorsPapin JA, Palsson BØ
JournalJournal of theoretical biology
PubMed Date2004 Mar 21
KeywordsAnimals, Cells, Computer Simulation, Models, Biological, Protein Binding, Signal Transduction

Signal transduction networks have only been studied at a small scale because large-scale reconstructions and suitable in silico analysis methods have not been available. Since reconstructions of large signaling networks are progressing well there is now a need to develop a framework for analysing structural properties of signaling networks. One such framework is presented here, one that is based on systemically independent pathways and a mass-balanced representation of signaling events. This approach was applied to a prototypic signaling network and it allowed for: (1) a systemic analysis of all possible input/output relationships, (2) a quantitative evaluation of network crosstalk, or the interconnectivity of systemically independent pathways, (3) a measure of the redundancy in the signaling network, (4) the participation of reactions in signaling pathways, and (5) the calculation of correlated reaction sets. These properties emerge from network structure and can only be derived and studied within a defined mathematical framework. The calculations presented are the first of their kind for a signaling network, while similar analysis has been extensively performed for prototypic and genome-scale metabolic networks. This approach does not yet account for dynamic concentration profiles. Due to the scalability of the stoichiometric formalism used, the results presented for the prototypic signaling network can be obtained for large signaling networks once their reconstruction is completed.

Alternate JournalJ. Theor. Biol.
PubMed ID14990392



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