Department of Mathematics and Computer Science, Bio Systems Analysis Group, Jena Centre for Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany.
Bioinformatics. 2009 Aug 1;25(15):1915-22. doi: 10.1093/bioinformatics/btp332. Epub 2009 May 25.
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.
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.
All data and a JAVA applet to check SBML-models is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.
Supplementary data are available at Bioinformatics online.
生物模型的数量和复杂性不断增加，使得自动检查模型属性和质量的程序变得非常必要。基本模式分析、通量平衡分析、缺陷分析和化学组织理论（OT）等方法只需要反应网络的计量结构，就可以推导出有价值的信息。然而，在系统生物学标记语言（SBML）等形式中，关于进行化学组织分析所需的化学计量系数的信息可能隐藏在动力学定律中。
首先，我们引入了一种算法，可以揭示反应网络动力学定律中可能隐藏的化学计量信息。这使我们能够使用修饰符将 OT 应用于 SBML 模型。其次，使用新算法，我们对包含在手动编辑的 BioModels 数据库中的 185 个模型进行了大规模分析。我们发现，在 41 个模型（22%）中，当正确考虑修饰符时，组织集发生变化。我们详细讨论了其中一个模型（BIOMD149，ERK 和 Wnt 信号通路的组合模型），当考虑修饰符时，其组织集发生了巨大变化。第三，我们在 5 个模型（3%）中发现了不一致，并确定了它们的特征。与通量方法相比，OT 能够更准确地识别那些在模型的长期模拟中可能存在的物种和反应[在 26 个案例（14%）中]。我们得出结论，我们的方法是一种有价值的工具，可以帮助提高生物模型及其存储库的一致性。
所有数据和一个用于检查 SBML 模型的 Java 小程序可从 http://www.minet.uni-jena.de/csb/prj/ot/tools 获得。
补充数据可在 Bioinformatics 在线获得。
