ArticleBioinformatics (Oxford, England)2009
Using chemical organization theory for model checking.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Modelling Worldviews as Stable Metabolisms.Entropy (Basel, Switzerland) · 2022Article
- Effects of small particle numbers on long-term behaviour in discrete biochemical systems.Bioinformatics (Oxford, England) · 2014Article
- Cycles and the qualitative evolution of chemical systems.PloS one · 2012Article
- Modeling formalisms in Systems Biology.AMB Express · 2011Article
- Article
- Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study.BMC systems biology · 2010Article
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3 authors.
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Abstract
motivationThe 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.
resultsFirst, 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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