Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A <i>Metamodel</i> Approach

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（Genome-scale metabolic models）通常存在不一致性，表现为阻断反应（blocked reactions）与间隙代谢物（gap metabolites）。为检测代谢模型中反复出现的不一致性，研究团队基于已公开的130个基因组规模代谢模型数据集开展了大规模分析。结果显示，约22%的反应在其存在的所有模型中均为阻断反应。为阐明此类不一致性的本质，研究人员将130个模型整合为单一网络，构建了元模型（metamodel）。研究采用非连通模块法对该元模型进行人工审定，随后将其作为参考网络，对每个独立的基因组规模代谢模型执行间隙填充（gap-filling）操作。最后，研究选取元模型构建过程中未纳入的36个模型作为概念验证样本，利用新的生化信息对元模型进行扩展，并评估其对间隙填充结果的影响。基于元模型的分析可得出以下结论：1）模型中发现的反复出现的不一致性，在模型重构过程所使用的代谢数据库中已存在；2）代谢数据库中存在的不一致性可传播至已重构的代谢模型；3）存在部分未表现为阻断反应，但因某些类型的人工伪影（artifact）而处于激活状态的反应；4）自动间隙填充的结果高度依赖于作为参考网络的元模型或代谢数据库的一致性与完整性。综上，应对代谢数据库开展一致性分析，以检测并填补间隙，同时检测并移除人工伪影与冗余信息。