title: Network-based scoring system for genome-scale metabolic reconstructions.
abstract: BACKGROUND
  Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood.
  
  
  RESULTS
  Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results.
  
  
  CONCLUSIONS
  We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.
authors: Departament de Química Física, Universitat de Barcelona, Martí i Franquès 1, Barcelona, 08028, Spain. marian.serrano@ub.edu
authors: M Ángeles
authors: MÁ
authors: Serrano
authors: Francesc
authors: F
authors: Sagués
references: BMC Syst Biol. 2008 Jul 11;2:61
references: 18616835
references: Proc Natl Acad Sci U S A. 2003 Apr 15;100(8):4372-6
references: 12676999
references: Nature. 2002 Nov 14;420(6912):190-3
references: 12432396
references: Nature. 2008 May 1;453(7191):98-101
references: 18451861
references: Phys Rev Lett. 2008 Feb 22;100(7):078701
references: 18352602
references: Science. 2002 Aug 30;297(5586):1551-5
references: 12202830
references: Trends Microbiol. 2005 Nov;13(11):550-8
references: 16169729
references: Proc Natl Acad Sci U S A. 2009 Dec 29;106(52):22073-8
references: 20018705
references: BMC Bioinformatics. 2010 Apr 29;11:213
references: 20426874
references: Science. 2009 Nov 27;326(5957):1263-8
references: 19965476
references: Nat Protoc. 2010 Jan;5(1):93-121
references: 20057383
references: Nucleic Acids Res. 2002 Mar 1;30(5):1163-8
references: 11861907
references: Nature. 2002 May 23;417(6887):399-403
references: 12000970
references: Nature. 2005 Feb 24;433(7028):895-900
references: 15729348
references: Mol Syst Biol. 2008;4:168
references: 18277384
references: Proc Biol Sci. 2001 Sep 7;268(1478):1803-10
references: 11522199
references: Nat Rev Genet. 2004 Feb;5(2):101-13
references: 14735121
references: Mol Syst Biol. 2007;3:121
references: 17593909
references: BMC Bioinformatics. 2006 Mar 29;7:177
references: 16571130
references: Proc Natl Acad Sci U S A. 2008 Sep 9;105(36):13223-8
references: 18765805
references: Radiology. 1982 Apr;143(1):29-36
references: 7063747
references: Nature. 2000 Oct 5;407(6804):651-4
references: 11034217
references: Mol Syst Biol. 2009;5:320
references: 19888215
references: Proc Natl Acad Sci U S A. 2002 Feb 19;99 Suppl 1:2566-72
references: 11875211
references: Proc Natl Acad Sci U S A. 2008 May 13;105(19):6795-6
references: 18474865
references: FEBS Lett. 2009 Dec 17;583(24):3923-30
references: 19879267
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Nov;68(5 Pt 2):056107
references: 14682846
references: Brief Bioinform. 2006 Sep;7(3):243-55
references: 16880171
references: Bioinformatics. 2003 Jan 22;19(2):270-7
references: 12538249
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Aug;64(2 Pt 2):026118
references: 11497662