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FastMM:一个用于个性化约束代谢建模的高效工具包。

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

机构信息

State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.

Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.

出版信息

BMC Bioinformatics. 2020 Feb 21;21(1):67. doi: 10.1186/s12859-020-3410-4.

Abstract

BACKGROUND

Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis.

RESULTS

Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA).

CONCLUSION

FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

摘要

背景

基于约束的代谢建模已被应用于理解代谢相关疾病机制、预测潜在的新药物靶点和抗代谢物,以及识别复杂疾病的生物标志物。尽管最先进的建模工具包 COBRA 3.0 功能强大,但它在进行通量平衡分析、敲除分析和马尔可夫链蒙特卡罗(MCMC)采样时需要大量的计算时间,这可能限制了它在大规模全基因组分析中的应用。

结果

在这里,我们使用 C/C++重写了 COBRA 3.0 的底层代码,并开发了一个工具包,称为 FastMM,用于有效地进行基于约束的代谢建模。结果表明,FastMM 在进行通量平衡分析和敲除分析时比 COBRA 3.0 快 2~400 倍,并返回一致的输出。当应用于 MCMC 采样时,FastMM 比 COBRA 3.0 快 8 倍。FastMM 也比一些高效的代谢建模应用程序,如 Cobrapy 和 Fast-SL 更快。此外,我们开发了一个用于快速代谢建模的 Matlab/Octave 接口。这个接口与 COBRA 3.0 完全兼容,使用户能够轻松地进行代谢建模的复杂应用。例如,没有深入的基于约束的代谢模型知识的用户只需在 Matlab/Octave 中输入一个命令即可执行个性化的代谢建模。用户还可以使用高级和多线程参数进行复杂的代谢建模。因此,我们提供了一种高效且用户友好的解决方案,用于进行大规模全基因组代谢建模。例如,FastMM 可应用于癌症基因组图谱(TCGA)中数百到数千个样本的个体癌症代谢谱建模。

结论

FastMM 是一个用于大规模个性化基于约束的代谢建模的高效且用户友好的工具包。它可以作为 COBRA 3.0 现有功能的补充和宝贵改进。FastMM 遵循 GPL 许可证,并可在 GitHub 网站上免费获得:https://github.com/GonghuaLi/FastMM。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ef0/7035665/2e8126caf56e/12859_2020_3410_Fig1_HTML.jpg

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