Literature DB >> 12466293

Extreme pathway lengths and reaction participation in genome-scale metabolic networks.

Jason A Papin1, Nathan D Price, Bernhard Ø Palsson.   

Abstract

Extreme pathways are a unique and minimal set of vectors that completely characterize the steady-state capabilities of genome-scale metabolic networks. A framework is provided to mathematically characterize extreme pathway length and to study how individual reactions participate in the extreme pathway structure of a network. The length of an extreme pathway is the number of reactions that comprise it. Reaction participation is the percentage of extreme pathways that utilize a given reaction. These properties were computed for the production of individual amino acids and protein production in Helicobacter pylori and individual amino acid production in Haemophilus influenzae. Reaction participation classifies the reactions into groups that are always, sometimes, or never utilized for the production of a target product. The utilized reactions can be further grouped into correlated subsets of reactions, some of which are non-obvious, and which may, in turn, suggest regulatory structure. The length of the extreme pathways did not correlate with product yield or chemical complexity. The distributions of extreme pathway lengths in H. pylori were also very different from those in H. influenzae, showing a distinct systemic difference between the two organisms, despite overall similar metabolic networks. Reaction participation and extreme pathway lengths thus serve to elucidate systemic biological features.

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Year:  2002        PMID: 12466293      PMCID: PMC187577          DOI: 10.1101/gr.327702

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  15 in total

1.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.

Authors:  S Schuster; D A Fell; T Dandekar
Journal:  Nat Biotechnol       Date:  2000-03       Impact factor: 54.908

2.  Global properties of the metabolic map of Escherichia coli.

Authors:  C A Ouzounis; P D Karp
Journal:  Genome Res       Date:  2000-04       Impact factor: 9.043

Review 3.  Metabolic modeling of microbial strains in silico.

Authors:  M W Covert; C H Schilling; I Famili; J S Edwards; I I Goryanin; E Selkov; B O Palsson
Journal:  Trends Biochem Sci       Date:  2001-03       Impact factor: 13.807

Review 4.  Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering.

Authors:  S Schuster; T Dandekar; D A Fell
Journal:  Trends Biotechnol       Date:  1999-02       Impact factor: 19.536

Review 5.  Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era.

Authors:  C H Schilling; S Schuster; B O Palsson; R Heinrich
Journal:  Biotechnol Prog       Date:  1999 May-Jun

6.  The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

Authors:  Jason A Papin; Nathan D Price; Jeremy S Edwards; Bernhard Ø Palsson B
Journal:  J Theor Biol       Date:  2002-03-07       Impact factor: 2.691

7.  Genome-scale metabolic model of Helicobacter pylori 26695.

Authors:  Christophe H Schilling; Markus W Covert; Iman Famili; George M Church; Jeremy S Edwards; Bernhard O Palsson
Journal:  J Bacteriol       Date:  2002-08       Impact factor: 3.490

8.  Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective.

Authors:  C H Schilling; D Letscher; B O Palsson
Journal:  J Theor Biol       Date:  2000-04-07       Impact factor: 2.691

9.  Computer-aided synthesis of biochemical pathways.

Authors:  M L Mavrovouniotis; G Stephanopoulos; G Stephanopoulos
Journal:  Biotechnol Bioeng       Date:  1990-12-20       Impact factor: 4.530

10.  Systems properties of the Haemophilus influenzae Rd metabolic genotype.

Authors:  J S Edwards; B O Palsson
Journal:  J Biol Chem       Date:  1999-06-18       Impact factor: 5.157

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  42 in total

1.  Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices.

Authors:  Nathan D Price; Jennifer L Reed; Jason A Papin; Iman Famili; Bernhard O Palsson
Journal:  Biophys J       Date:  2003-02       Impact factor: 4.033

2.  The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis.

Authors:  Jason A Papin; Bernhard O Palsson
Journal:  Biophys J       Date:  2004-07       Impact factor: 4.033

Review 3.  Computational tools for the synthetic design of biochemical pathways.

Authors:  Marnix H Medema; Renske van Raaphorst; Eriko Takano; Rainer Breitling
Journal:  Nat Rev Microbiol       Date:  2012-01-23       Impact factor: 60.633

4.  The geometry of the flux cone of a metabolic network.

Authors:  Clemens Wagner; Robert Urbanczik
Journal:  Biophys J       Date:  2005-09-23       Impact factor: 4.033

5.  Bayesian flux balance analysis applied to a skeletal muscle metabolic model.

Authors:  Jenni Heino; Knarik Tunyan; Daniela Calvetti; Erkki Somersalo
Journal:  J Theor Biol       Date:  2007-04-10       Impact factor: 2.691

Review 6.  Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies.

Authors:  Predrag Horvat; Martin Koller; Gerhart Braunegg
Journal:  World J Microbiol Biotechnol       Date:  2015-06-12       Impact factor: 3.312

7.  Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition.

Authors:  Christian L Barrett; Nathan D Price; Bernhard O Palsson
Journal:  BMC Bioinformatics       Date:  2006-03-13       Impact factor: 3.169

Review 8.  Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators.

Authors:  Francisco Llaneras; Jesús Picó
Journal:  J Biomed Biotechnol       Date:  2010-05-11

Review 9.  Applications of genome-scale metabolic reconstructions.

Authors:  Matthew A Oberhardt; Bernhard Ø Palsson; Jason A Papin
Journal:  Mol Syst Biol       Date:  2009-11-03       Impact factor: 11.429

10.  Trends in modeling Biomedical Complex Systems.

Authors:  Luciano Milanesi; Paolo Romano; Gastone Castellani; Daniel Remondini; Petro Liò
Journal:  BMC Bioinformatics       Date:  2009-10-15       Impact factor: 3.169

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