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

BMC Bioinformatics

Feifei Han¹  Wenzhong Xiao²  Shaoxing Dai³  Wenxing Li³  Gong-Hua Li³  Jingfei Huang⁴ 

[1] Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, 02114, Boston, MA, USA;Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, 02114, Boston, MA, USA;Stanford Genome Technology Center, Stanford University, 94304, Palo Alto, CA, USA;State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, 650223, Kunming, Yunnan, China;State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, 650223, Kunming, Yunnan, China;Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan, 650223, Kunming, Yunnan, China;

关键词: FastMM;    Constraint-based model;    Metabolic modeling;    Metabolism;   

DOI  :  10.1186/s12859-020-3410-4

来源: Springer

【 摘 要 】

BackgroundConstraint-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.ResultsHere, 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).ConclusionFastMM 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.

【 授权许可】

CC BY