PDF(Pubmed)
Abstract：
Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.
摘要：
基因组规模的代谢模型（GEM）是特定于生物体的知识库，可用于揭示致病性或改善生物技术应用中特定代谢物的生产。然而,在体外环境中细菌增殖预测的有效性几乎没有研究。方法：本工作结合了计算机和体外方法来创建和管理纹状体棒杆菌的菌株特异性基因组尺度代谢模型。结果：我们介绍了五个新创建的高质量菌株特异性基因组尺度代谢模型（GEMs），满足所有当代标准和要求。所有这些模型都使用社区标准测试套件代谢模型测试（MEMOTE）进行了基准测试，并通过实验室实验进行了验证。对于这些模型的策展，软件基础设施精炼GEM被开发为并行地对这些模型进行工作，并符合GEM的质量标准。实验数据证实了模型预测，并开发了基于倍增时间的新比较度量来量化细菌生长。讨论：未来的建模项目可以依赖于建议的软件，这与特定的环境条件无关。基于增长率计算的验证方法现在是可访问的，并且与生物学问题密切相关。精选的模型可以通过BioModels和GitHub存储库免费获得，并且可以使用。开源软件refineGEM可从https://github.com/draeger-lab/refinegems获得。
