Literature DB >> 12538249

Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms.

Hongwu Ma1, An-Ping Zeng.   

Abstract

MOTIVATION: Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed.
RESULTS: In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. AVAILABILITY: The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. SUPPLEMENTARY MATERIAL: Bioinformatics Online.

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Year:  2003        PMID: 12538249     DOI: 10.1093/bioinformatics/19.2.270

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  143 in total

1.  Dynamic generation and qualitative analysis of metabolic pathways by a joint database/graph theoretical approach.

Authors:  F Ehrentreich; D Schomburg
Journal:  Funct Integr Genomics       Date:  2003-10-16       Impact factor: 3.410

2.  The metabolic world of Escherichia coli is not small.

Authors:  Masanori Arita
Journal:  Proc Natl Acad Sci U S A       Date:  2004-02-02       Impact factor: 11.205

3.  In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism.

Authors:  Masanori Arita
Journal:  Genome Res       Date:  2003-10-14       Impact factor: 9.043

4.  Finding metabolic pathways using atom tracking.

Authors:  Allison P Heath; George N Bennett; Lydia E Kavraki
Journal:  Bioinformatics       Date:  2010-04-25       Impact factor: 6.937

5.  In silico identification of gene amplification targets for improvement of lycopene production.

Authors:  Hyung Seok Choi; Sang Yup Lee; Tae Yong Kim; Han Min Woo
Journal:  Appl Environ Microbiol       Date:  2010-03-26       Impact factor: 4.792

6.  Phylogeny of metabolic networks: a spectral graph theoretical approach.

Authors:  Krishanu Deyasi; Anirban Banerjee; Bony Deb
Journal:  J Biosci       Date:  2015-10       Impact factor: 1.826

Review 7.  Methods for biological data integration: perspectives and challenges.

Authors:  Vladimir Gligorijević; Nataša Pržulj
Journal:  J R Soc Interface       Date:  2015-11-06       Impact factor: 4.118

8.  Functional cartography of complex metabolic networks.

Authors:  Roger Guimerà; Luís A Nunes Amaral
Journal:  Nature       Date:  2005-02-24       Impact factor: 49.962

9.  Kinetic constraints for formation of steady states in biochemical networks.

Authors:  Junli Liu
Journal:  Biophys J       Date:  2005-02-24       Impact factor: 4.033

Review 10.  Complex networks and simple models in biology.

Authors:  Eric de Silva; Michael P H Stumpf
Journal:  J R Soc Interface       Date:  2005-12-22       Impact factor: 4.118

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