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

	FastMM: an efficient toolbox for personalized constraint-based metabolic modeling
	Li, Gong-Hua; Dai, Shaoxing; Han, Feifei; Li, Wenxin; Huang, Jingfei; Xiao, Wenzhong
	2020
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	21 期号:1
摘要	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 similar to 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.
收录类别	sci
语种	英语
文献类型	期刊论文
条目标识符	http://ir.kiz.ac.cn/handle/152453/12780
专题	科研部门_学习记忆的分子神经机制（徐林）
推荐引用方式 GB/T 7714	Li, Gong-Hua,Dai, Shaoxing,Han, Feifei,et al. FastMM: an efficient toolbox for personalized constraint-based metabolic modeling[J]. BMC BIOINFORMATICS,2020,21(1).
APA	Li, Gong-Hua,Dai, Shaoxing,Han, Feifei,Li, Wenxin,Huang, Jingfei,&Xiao, Wenzhong.(2020).FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.BMC BIOINFORMATICS,21(1).
MLA	Li, Gong-Hua,et al."FastMM: an efficient toolbox for personalized constraint-based metabolic modeling".BMC BIOINFORMATICS 21.1(2020).

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