Abstract
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.
Highlights
Metabolic flux analysis (MFA) is an important component of metabolic engineering (Stephanopoulos, 1999)
MFA measures the distribution of flux in the different metabolic pathways of an organism as well as identifies the intracellular flux changes associated with altered cellular response such as overproduction of a desired metabolite for biotechnological applications or understanding the altered pathways in diseases
We employed the models and the mass isotopomer distributions (MIDs) data given in OpenFLUX (Quek et al, 2009) for initial evaluation of FluxPyt
Summary
Metabolic flux analysis (MFA) is an important component of metabolic engineering (Stephanopoulos, 1999). The cells are required to be at metabolically steady state, i.e., when the intracellular concentrations of metabolites do not change over time (Zamboni et al, 2009) This ensures that the differences in the labeling patterns are not due to temporal variations in the fluxes, but due to the differential activity of the pathways. Most software for 13C-MFA such as OpenFLUX (Quek et al, 2009), WUFlux (He et al, 2016), INCA (Young, 2014) and Metran (Antoniewicz, Kelleher & Stephanopoulos, 2007a) work either on the proprietary MATLAB platform or are not free and open source. We believe that a truly open-source software in which all the components and packages are free and open source would make MFA more accessible to researchers and encourage further development of 13C-MFA and related tools
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
