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Bioinformatics Advance Access originally published online on May 25, 2009
Bioinformatics 2009 25(15):1915-1922; doi:10.1093/bioinformatics/btp332
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Using chemical organization theory for model checking

Christoph Kaleta 1,2,3,{dagger}, Stephan Richter 1,2,{dagger} and Peter Dittrich 1,2,*

1 Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB), 2 Department of Mathematics and Computer Science and 3 Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany

* To whom correspondence should be addressed.


  Abstract

Motivation: The increasing number and complexity of biomodels makes automatic procedures for checking the models' properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws.

Results: First, we introduce an algorithm that uncovers stoichiometric information that might be hidden in the kinetic laws of a reaction network. This allows us to apply OT to SBML models using modifiers. Second, using the new algorithm, we performed a large-scale analysis of the 185 models contained in the manually curated BioModels Database. We found that for 41 models (22%) the set of organizations changes when modifiers are considered correctly. We discuss one of these models in detail (BIOMD149, a combined model of the ERK- and Wnt-signaling pathways), whose set of organizations drastically changes when modifiers are considered. Third, we found inconsistencies in 5 models (3%) and identified their characteristics. Compared with flux-based methods, OT is able to identify those species and reactions more accurately [in 26 cases (14%)] that can be present in a long-term simulation of the model. We conclude that our approach is a valuable tool that helps to improve the consistency of biomodels and their repositories.

Availability: All data and a JAVA applet to check SBML-models is available from http://www.minet.uni-jena.de/csb/prj/ot/tools

Contact: dittrich{at}minet.uni-jena.de

Supplementary information: Supplementary data are available at Bioinformatics online.

{dagger} The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Associate Editor: Olga Troyanskaya

Received on July 22, 2008; revised on May 4, 2009; accepted on May 17, 2009

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