Literature DB >> 16986322

Challenges to be faced in the reconstruction of metabolic networks from public databases.

M G Poolman1, B K Bonde, A Gevorgyan, H H Patel, D A Fell.   

Abstract

In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database.

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Year:  2006        PMID: 16986322     DOI: 10.1049/ip-syb:20060012

Source DB:  PubMed          Journal:  Syst Biol (Stevenage)        ISSN: 1741-2471


  22 in total

1.  The strength of chemical linkage as a criterion for pruning metabolic graphs.

Authors:  Wanding Zhou; Luay Nakhleh
Journal:  Bioinformatics       Date:  2011-05-05       Impact factor: 6.937

2.  Challenges in experimental data integration within genome-scale metabolic models.

Authors:  Pierre-Yves Bourguignon; Areejit Samal; François Képès; Jürgen Jost; Olivier C Martin
Journal:  Algorithms Mol Biol       Date:  2010-04-22       Impact factor: 1.405

3.  Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways.

Authors:  Graham R Smith; Daryl P Shanley
Journal:  PLoS One       Date:  2010-06-14       Impact factor: 3.240

4.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions.

Authors:  Jan Schellenberger; Junyoung O Park; Tom M Conrad; Bernhard Ø Palsson
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

5.  Integration of metabolic databases for the reconstruction of genome-scale metabolic networks.

Authors:  Karin Radrich; Yoshimasa Tsuruoka; Paul Dobson; Albert Gevorgyan; Neil Swainston; Gino Baart; Jean-Marc Schwartz
Journal:  BMC Syst Biol       Date:  2010-08-16

6.  A genome-scale metabolic model of Arabidopsis and some of its properties.

Authors:  Mark G Poolman; Laurent Miguet; Lee J Sweetlove; David A Fell
Journal:  Plant Physiol       Date:  2009-09-15       Impact factor: 8.340

7.  Flux balance analysis of genome-scale metabolic model of rice (Oryza sativa): aiming to increase biomass.

Authors:  Rahul Shaw; Sudip Kundu
Journal:  J Biosci       Date:  2015-10       Impact factor: 1.826

8.  Responses to light intensity in a genome-scale model of rice metabolism.

Authors:  Mark G Poolman; Sudip Kundu; Rahul Shaw; David A Fell
Journal:  Plant Physiol       Date:  2013-05-02       Impact factor: 8.340

9.  A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

Authors:  Markus J Herrgård; Neil Swainston; Paul Dobson; Warwick B Dunn; K Yalçin Arga; Mikko Arvas; Nils Blüthgen; Simon Borger; Roeland Costenoble; Matthias Heinemann; Michael Hucka; Nicolas Le Novère; Peter Li; Wolfram Liebermeister; Monica L Mo; Ana Paula Oliveira; Dina Petranovic; Stephen Pettifer; Evangelos Simeonidis; Kieran Smallbone; Irena Spasić; Dieter Weichart; Roger Brent; David S Broomhead; Hans V Westerhoff; Betül Kirdar; Merja Penttilä; Edda Klipp; Bernhard Ø Palsson; Uwe Sauer; Stephen G Oliver; Pedro Mendes; Jens Nielsen; Douglas B Kell
Journal:  Nat Biotechnol       Date:  2008-10       Impact factor: 54.908

10.  Simple topological properties predict functional misannotations in a metabolic network.

Authors:  Rodrigo Liberal; John W Pinney
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

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