Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.
LABBELS -Associate Laboratory, Braga, Guimarães, Portugal.
J Integr Bioinform. 2022 Sep 5;19(3). doi: 10.1515/jib-2022-0014. eCollection 2022 Sep 1.
Genome-scale metabolic models (GEMs) are essential tools for phenotype prediction and strain optimisation. The most straightforward GEMs reconstruction approach uses published models as templates to generate the initial draft, requiring further curation. Such an approach is used by BiGG Integration Tool (BIT), available for users. This tool uses models from BiGG Models database as templates for the draft models. Moreover, BIT allows the selection between different template combinations. The main objective of this study is to assess the draft models generated using this tool and compare them BIT, comparing these to CarveMe models, both of which use the BiGG database, and curated models. For this, three organisms were selected, namely , and The models' variability was assessed using reactions and genes' metabolic functions. This study concluded that models generated with BIT for each organism were differentiated, despite sharing a significant portion of metabolic functions. Furthermore, the template seems to influence the content of the models, though to a lower extent. When comparing each draft with curated models, BIT had better performances than CarveMe in all metrics. Hence, BIT can be considered a fast and reliable alternative for draft reconstruction for bacteria models.
基因组规模代谢模型（GEMs）是表型预测和菌株优化的重要工具。最直接的 GEMs 重建方法是使用已发表的模型作为模板来生成初始草案，这需要进一步的整理。BiGG 整合工具（BIT）就采用了这种方法，供用户使用。该工具使用 BiGG 模型数据库中的模型作为草案模型的模板。此外，BIT 还允许在不同的模板组合之间进行选择。本研究的主要目的是评估使用该工具生成的草案模型，并将其与 BIT 进行比较，同时与 CarveMe 模型进行比较，这两个模型都使用 BiGG 数据库和已整理的模型。为此，选择了三个生物体，即 、 和 。使用反应和基因的代谢功能评估了模型的可变性。这项研究得出的结论是，尽管共享了大量的代谢功能，但为每个生物体生成的 BIT 模型都是有区别的。此外，模板似乎会影响模型的内容，但影响程度较低。在将每个草案与已整理的模型进行比较时，BIT 在所有指标上的性能都优于 CarveMe。因此，BIT 可以被认为是细菌模型草案重建的快速可靠的替代方法。
