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Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


The upper panel shows a textual description of a reaction. The lower panel shows the bipartite graph modelling of this reaction. Blue nodes are metabolite and the pink node is the reaction. Arrows indicate the direction of a reaction, but in that case the reaction is reversible which is indicated by red border of the reaction node
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Fig3: The upper panel shows a textual description of a reaction. The lower panel shows the bipartite graph modelling of this reaction. Blue nodes are metabolite and the pink node is the reaction. Arrows indicate the direction of a reaction, but in that case the reaction is reversible which is indicated by red border of the reaction node

Mentions: The metabolic network was modelled as a bipartite graph, which is composed of two types of nodes: corresponding here respectively to the reactions and to the metabolites (see Fig. 3). There is an edge between a metabolite and a reaction node if the metabolite is a substrate of the reaction, and there is an edge between a reaction and a metabolite node if the reaction produces the metabolite. Each edge has an arrow indicating its relation to the reaction and reaction nodes are filled with a colour according to their reversibility. Unlike simple metabolic graphs, which are characterised by a single type of node (compounds or reactions), the models represented here depict the complete linkage pattern between the set of substrates and the set of products of a reaction. In those cases where the direction of the reaction is not strictly defined in the metabolic databases, the direction is determined as follows: if a reaction occurs in the same direction, whatever the metabolic pathway in MetaCyc, then the reaction is considered irreversible and a direction is assigned. Conversely, if a reaction occurs in two directions according to MetaCyc pathway attributes, then the reaction is assigned as reversible. As any reaction can be reversible under the correct thermodynamic conditions we confine ourselves, in this first approximation, to database assignments on reversibility, although manual curation is possible.Fig. 3


Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

The upper panel shows a textual description of a reaction. The lower panel shows the bipartite graph modelling of this reaction. Blue nodes are metabolite and the pink node is the reaction. Arrows indicate the direction of a reaction, but in that case the reaction is reversible which is indicated by red border of the reaction node
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2874485&req=5

Fig3: The upper panel shows a textual description of a reaction. The lower panel shows the bipartite graph modelling of this reaction. Blue nodes are metabolite and the pink node is the reaction. Arrows indicate the direction of a reaction, but in that case the reaction is reversible which is indicated by red border of the reaction node
Mentions: The metabolic network was modelled as a bipartite graph, which is composed of two types of nodes: corresponding here respectively to the reactions and to the metabolites (see Fig. 3). There is an edge between a metabolite and a reaction node if the metabolite is a substrate of the reaction, and there is an edge between a reaction and a metabolite node if the reaction produces the metabolite. Each edge has an arrow indicating its relation to the reaction and reaction nodes are filled with a colour according to their reversibility. Unlike simple metabolic graphs, which are characterised by a single type of node (compounds or reactions), the models represented here depict the complete linkage pattern between the set of substrates and the set of products of a reaction. In those cases where the direction of the reaction is not strictly defined in the metabolic databases, the direction is determined as follows: if a reaction occurs in the same direction, whatever the metabolic pathway in MetaCyc, then the reaction is considered irreversible and a direction is assigned. Conversely, if a reaction occurs in two directions according to MetaCyc pathway attributes, then the reaction is assigned as reversible. As any reaction can be reversible under the correct thermodynamic conditions we confine ourselves, in this first approximation, to database assignments on reversibility, although manual curation is possible.Fig. 3

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.