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

2020 ◽  
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 ◽  
Link Type ◽  
Cross Links ◽  
Genome Scale

ABSTRACTMetaNetX/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 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 Plant Metabolic Network: A multi-species resource of plant metabolic information

2021 ◽  
Author(s):  
Charles Hawkins ◽  
Daniel Ginzburg ◽  
Kangmei Zhao ◽  
William Dwyer ◽  
Bo Xue ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Multiple Correspondence Analysis ◽  
Model Organisms ◽  
Plant Metabolism ◽  
Online Resource ◽  
Transcriptomics Data ◽  
Cell Type Specific ◽  
Genome Scale ◽  
Metabolic Information

AbstractPlant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

scholarly journals Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

2019 ◽  
Author(s):  
Maureen A. Carey ◽  
Gregory L. Medlock ◽  
Michał Stolarczyk ◽  
William A. Petri ◽  
Jennifer L. Guler ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Protozoan Parasite ◽  
Global Impacts ◽  
Metabolic Models ◽  
Species Specific ◽  
Experimental Findings ◽  
Eukaryotic Organisms ◽  
Genome Scale

AbstractProtozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

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 Architect: a tool for producing high-quality metabolic models through improved enzyme annotation

2021 ◽  
Author(s):  
Nirvana Nursimulu ◽  
Alan Moses ◽  
John Parkinson
Keyword(s):  
Metabolic Network ◽  
Sequence Similarity ◽  
Metabolic Model ◽  
High Quality ◽  
Ensemble Approach ◽  
Metabolic Models ◽  
Improved Performance ◽  
Constraints Based Modeling ◽  
Sbml Format

Motivation: Constraints-based modeling is a powerful framework for understanding growth of organisms. Results from such simulation experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. Results: We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools. Availability: Code for Architect is available at https://github.com/ParkinsonLab/Architect.

Building and analysing genome-scale metabolic models

2010 ◽  
Vol 38 (5) ◽  
pp. 1197-1201 ◽  
Author(s):  
David A. Fell ◽  
Mark G. Poolman ◽  
Albert Gevorgyan
Keyword(s):  
Arabidopsis Thaliana ◽  
Metabolic Network ◽  
Streptococcus Agalactiae ◽  
Model Building ◽  
Data Sources ◽  
Building Process ◽  
Metabolic Models ◽  
Genome Scale ◽  
Model Building Process ◽  
Do So

Reconstructing a model of the metabolic network of an organism from its annotated genome sequence would seem, at first sight, to be one of the most straightforward tasks in functional genomics, even if the various data sources required were never designed with this application in mind. The number of genome-scale metabolic models is, however, lagging far behind the number of sequenced genomes and is likely to continue to do so unless the model-building process can be accelerated. Two aspects that could usefully be improved are the ability to find the sources of error in a nascent model rapidly, and the generation of tenable hypotheses concerning solutions that would improve a model. We will illustrate these issues with approaches we have developed in the course of building metabolic models of Streptococcus agalactiae and Arabidopsis thaliana.

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.

Non-steady variations of SOD and phosphate release rate due to changes in the quality of the overlying water

2000 ◽  
Vol 42 (3-4) ◽  
pp. 265-272 ◽  
Author(s):  
T. Inoue ◽  
Y. Nakamura ◽  
Y. Adachi
Keyword(s):  
Release Rate ◽  
Oxygen Demand ◽  
Phosphate Concentration ◽  
Sediment Oxygen Demand ◽  
Biochemical Reactions ◽  
Overlying Water ◽  
Phosphate Release ◽  
Do Concentration ◽  
The Difference

A dynamic model, which predicts non-steady variations in the sediment oxygen demand (SOD) and phosphate release rate, has been designed. This theoretical model consists of three diffusion equations with biochemical reactions for dissolved oxygen (DO), phosphate and ferrous iron. According to this model, step changes in the DO concentration and flow velocity produce drastic changes in the SOD and phosphate release rate within 10 minutes. The vigorous response of the SOD and phosphate release rate is caused by the difference in the time scale of diffusion in the water boundary layer and that of the biochemical reactions in the sediment. Secondly, a negative phosphate transfer from water to sediment can even occur under aerobic conditions. This is caused by the decrease in phosphate concentration in the aerobic layer due to adsorption.

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