scholarly journals Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

2018 ◽  
Author(s):  
Nhung Pham ◽  
Ruben Van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  
Keyword(s):  
Systems Medicine ◽  
Biochemical Reactions ◽  
Metabolic Modelling ◽  
Research Areas ◽  
Limit Model ◽  
Metabolic Models ◽  
Genome Scale ◽  
Manual Verification

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

scholarly journals Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Metabolites ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 28 ◽  
Author(s):  
Nhung Pham ◽  
Ruben van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  
Keyword(s):  
Systems Medicine ◽  
Biochemical Reactions ◽  
Metabolic Modelling ◽  
Research Areas ◽  
Limit Model ◽  
Metabolic Models ◽  
Genome Scale ◽  
Manual Verification

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

scholarly journals RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

2019 ◽  
Author(s):  
Cheng Zhang ◽  
Sunjae Lee ◽  
Gholamreza Bidkhori ◽  
Rui Benfeitas ◽  
Alen Lovric ◽  
...  
Keyword(s):  
Case Studies ◽  
State Of The Art ◽  
Differentially Expressed ◽  
Systems Medicine ◽  
Transcriptomics Data ◽  
Altered Metabolism ◽  
Metabolic Models ◽  
Genome Scale ◽  
Potential Use ◽  
Single Set

AbstractRelative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

scholarly journals MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

2020 ◽  
Vol 49 (D1) ◽  
pp. D570-D574
Author(s):  
Sébastien Moretti ◽  
Van Du T Tran ◽  
Florence Mehl ◽  
Mark Ibberson ◽  
Marco Pagni
Keyword(s):  
Metabolic Network ◽  
Intrinsic Properties ◽  
Biochemical Reactions ◽  
Online Services ◽  
Major Improvement ◽  
Sparql Endpoint ◽  
Cross Links ◽  
Metabolic Models ◽  
Genome Scale

Abstract MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

scholarly journals Inferring biochemical reactions and metabolite structures to cope with metabolic pathway drift

2018 ◽  
Author(s):  
Arnaud Belcour ◽  
Jean Girard ◽  
Méziane Aite ◽  
Ludovic Delage ◽  
Camille Trottier ◽  
...  
Keyword(s):  
Metabolic Pathway ◽  
Metabolic Pathways ◽  
Logical Reasoning ◽  
Model Organisms ◽  
Enzymatic Reactions ◽  
Proof Of Concept ◽  
Biochemical Reactions ◽  
Metabolomic Data ◽  
Metabolic Models ◽  
Genome Scale

AbstractInferring genome-scale metabolic networks in emerging model organisms is challenging because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution. This limits the possibility to automatically transfer knowledge from well-established model organisms. Therefore, specific bioinformatic tools are necessary to infer new biochemical reactions and new metabolic structures that can be checked experimentally. Using an integrative approach combining both genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterol or mycosporine-like amino acids (MAA) synthesis pathways, undergo substantial turnover. This phenomenon, which we formally define as “metabolic pathway drift”, is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a new methodological approach to formalize the logical reasoning necessary to infer new reactions and new molecular structures, based on previous biochemical knowledge. We use this approach to infer previously unknown reactions in the sterol and MAA pathways.Author summaryGenome-scale metabolic models describe our current understanding of all metabolic pathways occuring in a given organism. For emerging model species, where few biochemical data are available about really occurring enzymatic activities, such metabolic models are mainly based on transferring knowledge from other more studied species, based on the assumption that the same genes have the same function in the compared species. However, integration of metabolomic data into genome-scale metabolic models leads to situations where gaps in pathways cannot be filled by known enzymatic reactions from existing databases. This is due to structural variation in metabolic pathways accross evolutionary time. In such cases, it is necessary to use complementary approaches to infer new reactions and new metabolic intermediates using logical reasoning, based on available partial biochemical knowledge. Here we present a proof of concept that this is feasible and leads to hypotheses that are precise enough to be a starting point for new experimental work.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan
Keyword(s):  
Arabidopsis Thaliana ◽  
Flux Balance Analysis ◽  
Critical Assessment ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Living Organisms ◽  
Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
Author(s):  
Xingxing Jian ◽  
Ningchuan Li ◽  
Qian Chen ◽  
Qiang Hua
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Gene Amplification ◽  
Metabolic Networks ◽  
Computational Analysis ◽  
Succinate Production ◽  
Novel Gene ◽  
Metabolic Models ◽  
Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

scholarly journals A metabolite-centric view on flux distributions in genome-scale metabolic models

2013 ◽  
Vol 7 (1) ◽  
pp. 33 ◽  
Author(s):  
S Riemer ◽  
René Rex ◽  
Dietmar Schomburg
Keyword(s):  
Metabolic Models ◽  
Genome Scale
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