scholarly journals OpenMebius: An Open Source Software for Isotopically Nonstationary13C-Based Metabolic Flux Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shuichi Kajihata ◽  
Chikara Furusawa ◽  
Fumio Matsuda ◽  
Hiroshi Shimizu
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Simulated Data ◽  
Isotopic Labeling ◽  
Metabolic Model ◽  
Cell Physiology ◽  
Flux Analysis

Thein vivomeasurement of metabolic flux by13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA.

scholarly journals 2H and 13C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis

2019 ◽  
Vol 54 ◽  
pp. 301-316 ◽  
Author(s):  
Tyler B. Jacobson ◽  
Paul A. Adamczyk ◽  
David M. Stevenson ◽  
Matthew Regner ◽  
John Ralph ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Zymomonas Mobilis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis

Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 232-240
Author(s):  
Axel Theorell ◽  
Katharina Nöh
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Bayesian Model Averaging ◽  
Linear Models ◽  
Model Averaging ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Large Set ◽  
Vast Number

Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals mfapy: An open-source Python package for 13C-based metabolic flux analysis

2021 ◽  
pp. e00177
Author(s):  
Fumio Matsuda ◽  
Kohsuke Maeda ◽  
Takeo Taniguchi ◽  
Yuya Kondo ◽  
Futa Yatabe ◽  
...  
Keyword(s):  
Open Source ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Python Package

scholarly journals FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4716 ◽  
Author(s):  
Trunil S. Desai ◽  
Shireesh Srivastava
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Strain Analysis ◽  
Monte Carlo Analysis ◽  
Reaction Rates ◽  
Flux Analysis ◽  
Analysis And Design

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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