Gong-Hua Li1, Shaoxing Dai1, Feifei Han2, Wenxing Li1, Jingfei Huang3,4, Wenzhong Xiao5,6. 1. State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. 2. Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. 3. State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. huangjf@mail.kiz.ac.cn. 4. Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan, Kunming, 650223, Yunnan, China. huangjf@mail.kiz.ac.cn. 5. Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. wenzhong.xiao@mgh.harvard.edu. 6. Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA. wenzhong.xiao@mgh.harvard.edu.
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.
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.
Authors: Ines Thiele; Neil Swainston; Ronan M T Fleming; Andreas Hoppe; Swagatika Sahoo; Maike K Aurich; Hulda Haraldsdottir; Monica L Mo; Ottar Rolfsson; Miranda D Stobbe; Stefan G Thorleifsson; Rasmus Agren; Christian Bölling; Sergio Bordel; Arvind K Chavali; Paul Dobson; Warwick B Dunn; Lukas Endler; David Hala; Michael Hucka; Duncan Hull; Daniel Jameson; Neema Jamshidi; Jon J Jonsson; Nick Juty; Sarah Keating; Intawat Nookaew; Nicolas Le Novère; Naglis Malys; Alexander Mazein; Jason A Papin; Nathan D Price; Evgeni Selkov; Martin I Sigurdsson; Evangelos Simeonidis; Nikolaus Sonnenschein; Kieran Smallbone; Anatoly Sorokin; Johannes H G M van Beek; Dieter Weichart; Igor Goryanin; Jens Nielsen; Hans V Westerhoff; Douglas B Kell; Pedro Mendes; Bernhard Ø Palsson Journal: Nat Biotechnol Date: 2013-03-03 Impact factor: 54.908
Authors: Laurent Heirendt; Sylvain Arreckx; Thomas Pfau; Sebastián N Mendoza; Anne Richelle; Almut Heinken; Hulda S Haraldsdóttir; Jacek Wachowiak; Sarah M Keating; Vanja Vlasov; Stefania Magnusdóttir; Chiam Yu Ng; German Preciat; Alise Žagare; Siu H J Chan; Maike K Aurich; Catherine M Clancy; Jennifer Modamio; John T Sauls; Alberto Noronha; Aarash Bordbar; Benjamin Cousins; Diana C El Assal; Luis V Valcarcel; Iñigo Apaolaza; Susan Ghaderi; Masoud Ahookhosh; Marouen Ben Guebila; Andrejs Kostromins; Nicolas Sompairac; Hoai M Le; Ding Ma; Yuekai Sun; Lin Wang; James T Yurkovich; Miguel A P Oliveira; Phan T Vuong; Lemmer P El Assal; Inna Kuperstein; Andrei Zinovyev; H Scott Hinton; William A Bryant; Francisco J Aragón Artacho; Francisco J Planes; Egils Stalidzans; Alejandro Maass; Santosh Vempala; Michael Hucka; Michael A Saunders; Costas D Maranas; Nathan E Lewis; Thomas Sauter; Bernhard Ø Palsson; Ines Thiele; Ronan M T Fleming Journal: Nat Protoc Date: 2019-03 Impact factor: 13.491