scholarly journals ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Esa Pitkänen ◽  
Arto Åkerlund ◽  
Ari Rantanen ◽  
Paula Jouhten ◽  
Esko Ukkonen
Keyword(s):  
Data Integration ◽  
Network Model ◽  
Metabolic Flux Analysis ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Flux Analysis ◽  
Stoichiometric Matrix ◽  
Web Based ◽  
Integration Problem

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/.

scholarly journals Assessing and Resolving Model Misspecifications in Metabolic Flux Analysis

2017 ◽  
Author(s):  
Rudiyanto Gunawan ◽  
Sandro Hutter
Keyword(s):  
Metabolic Flux Analysis ◽  
Iterative Procedure ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Statistical Tests ◽  
Chinese Hamster ◽  
Least Square ◽  
Flux Analysis ◽  
Stoichiometric Matrix ◽  
F Test

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.

scholarly journals Validity of natural isotope abundance correction for metabolic flux analysis

2020 ◽  
Author(s):  
Roland Nilsson
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Network Models ◽  
Flux Analysis ◽  
Binomial Distributions ◽  
Linear Transform ◽  
Natural Isotope Abundance ◽  
Natural Isotopes ◽  
Detailed Derivation ◽  
Mathematical Derivation

AbstractA pervasive issue in stable isotope tracing and metabolic flux analysis is the presence of naturally occurring isotopes such as 13C. For mass isotopomer distributions (MIDs) measured by mass spectrometry, it is common practice to correct for natural isotopes within metabolites of interest using a particular linear transform based on binomial distributions. However, the origin and mathematical derivation of this transform is rather obscure, and it may be difficult for nonexperts to understand precisely how to interpret the resulting corrected MIDs. Moreover, corrected MIDs are often used to fit metabolic network models and infer metabolic fluxes, which implicitly assumes that corrected MIDs will yield the same flux solution as the actual observed MIDs. Yet, there seems to be no published proof of this important property. Here, we provide a detailed derivation of the MID-correcting linear transform, reflecting its historical development, and describe some interesting properties. We also provide a proof that for metabolic flux analysis on noise-free MID data at steady state, observed and corrected MIDs indeed yield the same flux solution. On the other hand, for noisy MID data, the flux solution will generally differ between the two representations.

scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals Dynamic metabolic flux analysis of underdetermined and overdetermined metabolic networks

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