Literature DB >> 12051985

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

Jason A Papin1, Nathan D Price, Jeremy S Edwards, Bernhard Ø Palsson B.   

Abstract

Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale. Copyright 2002 Elsevier Science Ltd.

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Year:  2002        PMID: 12051985     DOI: 10.1006/jtbi.2001.2499

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  29 in total

Review 1.  Thirteen years of building constraint-based in silico models of Escherichia coli.

Authors:  Jennifer L Reed; Bernhard Ø Palsson
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2.  Extreme pathway lengths and reaction participation in genome-scale metabolic networks.

Authors:  Jason A Papin; Nathan D Price; Bernhard Ø Palsson
Journal:  Genome Res       Date:  2002-12       Impact factor: 9.043

3.  Flux coupling analysis of genome-scale metabolic network reconstructions.

Authors:  Anthony P Burgard; Evgeni V Nikolaev; Christophe H Schilling; Costas D Maranas
Journal:  Genome Res       Date:  2004-01-12       Impact factor: 9.043

4.  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

5.  Extreme pathway analysis of human red blood cell metabolism.

Authors:  Sharon J Wiback; Bernhard O Palsson
Journal:  Biophys J       Date:  2002-08       Impact factor: 4.033

6.  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

7.  C4GEM, a genome-scale metabolic model to study C4 plant metabolism.

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Review 8.  Biochemical and statistical network models for systems biology.

Authors:  Nathan D Price; Ilya Shmulevich
Journal:  Curr Opin Biotechnol       Date:  2007-08-03       Impact factor: 9.740

9.  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

10.  Initial proteome analysis of model microorganism Haemophilus influenzae strain Rd KW20.

Authors:  Eugene Kolker; Samuel Purvine; Michael Y Galperin; Serg Stolyar; David R Goodlett; Alexey I Nesvizhskii; Andrew Keller; Tao Xie; Jimmy K Eng; Eugene Yi; Leroy Hood; Alex F Picone; Tim Cherny; Brian C Tjaden; Andrew F Siegel; Thomas J Reilly; Kira S Makarova; Bernhard O Palsson; Arnold L Smith
Journal:  J Bacteriol       Date:  2003-08       Impact factor: 3.490

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