Insights into plant metabolic networks from steady-state metabolic flux analysis

Biochimie ◽  
2009 ◽  
Vol 91 (6) ◽  
pp. 697-702 ◽  
Author(s):  
Nicholas J. Kruger ◽  
R. George Ratcliffe
Author(s):  
Brian D. Follstad ◽  
R. Robert Balcarcel ◽  
Gregory Stephanopoulos ◽  
Daniel I. C. Wang

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.


2016 ◽  
Vol 49 (26) ◽  
pp. 318-323
Author(s):  
Sofia Fernandes ◽  
Julien Robitaille ◽  
Georges Bastin ◽  
Mario Jolicoeur ◽  
Alain Vande Wouwer

Author(s):  
Rudiyanto Gunawan ◽  
Sandro Hutter

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.


2020 ◽  
Author(s):  
Huan Li ◽  
Min Chen ◽  
Peng Liu ◽  
Shuai Wang ◽  
JY Xia

Abstract Crabtree effect is well known for Saccharomyces cerevisiae, and is defined as glucose-induced repression of respiratory flux. Even though a number of hypotheses have been formulated, its triggering mechanisms are still unknown. At present, the information about intracellular metabolic flux can be obtained by the 13C isotope labeling experiments. 13C metabolic flux analysis(13C-MFA) is a traditional method for calculating metabolic flux based on isotopic steady state. Another new method (INST-13C-MFA: Isotopically nonstationary metabolic flux analysis) based on isotope non-steady state is being used by researchers. In this review, we have chemostatized S. cerevisiae at three different dilution rates (D=0.12, 0.22, 0.32 h-1) and obtained the metabolic flux distribution of the intracellular central carbon metabolic of S. cerevisiae using INST-13C-MFA. Combined with the metabolome and metabolic fluxome data, we found obvious metabolic flux shift under the three different physiological states. In this process, pyruvate decarboxylase, ethanol dehydrogenase and acetyl-CoA synthase(AcCoA) catalyzed reactions were key points. Negative correlation between relative flux of embden meyerh of pathway(EMP) and tricarboxylic acid cycle(TCA) and biomass yield, while positive correlation for pentose phosphate pathway(PPP) were observed. Yield of acetate and glycerol did not change significantly, while that of ethanol increased sharply. In the central carbon metabolism (CCM), most of the carbon flux (70%) was directed to the EMP. At the same time, the energy charge increased with dilution rate, and the cell's energy supply mode gradually shifted from oxidative respiration to substrate level phosphorylation mode.


2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Esa Pitkänen ◽  
Arto Åkerlund ◽  
Ari Rantanen ◽  
Paula Jouhten ◽  
Esko Ukkonen

Summary ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitateThe construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced inReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool.The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.


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