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

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 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 METAMDB: METABOLIC ATOM MAPPING DATABASE

2021 ◽  
Author(s):  
Collin Starke ◽  
Andre Wegner
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Analysis Software ◽  
13C Metabolic Flux Analysis ◽  
Atom Mapping ◽  
Metabolic Reactions ◽  
Genome Scale ◽  
Mapping Models

MetAMDB (https://metamdb.tu-bs.de/) is an open source metabolic atom mapping database, providing atom mappings for around 75000 metabolic reactions. Each atom mapping can be inspected and downloaded either as a RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome scale) and can subsequently be used in standard 13C metabolic flux analysis software.

scholarly journals Bridging the Gap between Fluxomics and Industrial Biotechnology

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Xueyang Feng ◽  
Lawrence Page ◽  
Jacob Rubens ◽  
Lauren Chircus ◽  
Peter Colletti ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Regulation ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Enzymatic Reaction ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
Potential Applications ◽  
Balance Analysis ◽  
Precise Prediction

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity.13C-based metabolic flux analysis (13C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both13C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

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