Ponce-de-Leon Miguel, Calle-Espinosa Jorge, Peretó Juli, Montero Francisco
Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain.
Departament de Bioquímica i Biologia Molecular and Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, C/José Beltrán 2, Paterna 46980, Spain.
PLoS One. 2015 Dec 2;10(12):e0143626. doi: 10.1371/journal.pone.0143626. eCollection 2015.
Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.
基因组规模代谢模型通常包含表现为受阻反应和缺口代谢物的不一致性。为了检测代谢模型中反复出现的不一致性，使用之前发表的包含130个基因组规模模型的数据集进行了大规模分析。结果表明，大量反应（约22%）在所有存在它们的模型中都是受阻的。为了揭示这种不一致性的本质，通过将130个模型连接成一个单一网络构建了一个元模型。该元模型使用未连接模块方法进行人工整理，然后用作参考网络对每个单独的基因组规模模型进行缺口填充。最后，使用一组在构建元模型时未考虑的36个模型，作为概念验证，用新的生化信息扩展元模型，并评估其对缺口填充结果的影响。对元模型进行的分析得出以下结论：1）在模型中发现的反复出现的不一致性在重建过程中使用的代谢数据库中已经存在；2）代谢数据库中不一致性的存在可以传播到重建模型中；3）存在一些未表现为受阻的反应，它们由于某些类别的人为因素而活跃；4）自动缺口填充的结果高度依赖于用作参考网络的元模型或代谢数据库的一致性和完整性。总之，应将一致性分析应用于代谢数据库，以检测和填补缺口，以及检测和去除人为因素和冗余信息。
