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FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses.
Desai, Trunil S; Srivastava, Shireesh.
Affiliation
  • Desai TS; Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.
  • Srivastava S; DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.
PeerJ ; 6: e4716, 2018.
Article in En | MEDLINE | ID: mdl-29736347
13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PeerJ Year: 2018 Document type: Article Affiliation country: India Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PeerJ Year: 2018 Document type: Article Affiliation country: India Country of publication: United States