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FastMM: an efficient toolbox for personalized constraint-based metabolic modeling
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 req...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035665/ https://www.ncbi.nlm.nih.gov/pubmed/32085724 http://dx.doi.org/10.1186/s12859-020-3410-4 |
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author | Li, Gong-Hua Dai, Shaoxing Han, Feifei Li, Wenxing Huang, Jingfei Xiao, Wenzhong |
author_facet | Li, Gong-Hua Dai, Shaoxing Han, Feifei Li, Wenxing Huang, Jingfei Xiao, Wenzhong |
author_sort | Li, Gong-Hua |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7035665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70356652020-02-27 FastMM: an efficient toolbox for personalized constraint-based metabolic modeling Li, Gong-Hua Dai, Shaoxing Han, Feifei Li, Wenxing Huang, Jingfei Xiao, Wenzhong BMC Bioinformatics Software 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. BioMed Central 2020-02-21 /pmc/articles/PMC7035665/ /pubmed/32085724 http://dx.doi.org/10.1186/s12859-020-3410-4 Text en © The Author(s). 2020, corrected publication 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Li, Gong-Hua Dai, Shaoxing Han, Feifei Li, Wenxing Huang, Jingfei Xiao, Wenzhong FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title | FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title_full | FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title_fullStr | FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title_full_unstemmed | FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title_short | FastMM: an efficient toolbox for personalized constraint-based metabolic modeling |
title_sort | fastmm: an efficient toolbox for personalized constraint-based metabolic modeling |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035665/ https://www.ncbi.nlm.nih.gov/pubmed/32085724 http://dx.doi.org/10.1186/s12859-020-3410-4 |
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