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  Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules

Saez-Rodriguez, J., Gayer, S., Ginkel, M., & Gilles, E. D. (2008). Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules. Bioinformatics, 24(16), i213-i219. doi:10.1093/bioinformatics/btn289.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-950F-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-BE9F-4
Genre: Journal Article

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Saez-Rodriguez, J.1, Author              
Gayer, S.1, Author              
Ginkel, Martin1, Author              
Gilles, E. D.1, Author              
Affiliations:
1Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738155              

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 Abstract: Abstract: Motivation: The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks defined as chemical systems. Such a decomposition would be very useful as most quantitative models are defined using the latter, more detailed formalism. Results: Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. © The Author 2008. Published by Oxford University Press. All rights reserved. [accessed October 30, 2008] Availability: The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot Supplementary information: Supplementary data are available at Bioinformatics online.

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 Dates: 2008
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: eDoc: 379996
DOI: 10.1093/bioinformatics/btn289
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 24 (16) Sequence Number: - Start / End Page: i213 - i219 Identifier: ISSN: 1367-4803
CoNE: /journals/resource/954926969991