title: Subnetwork hierarchies of biochemical pathways.
abstract: BACKGROUND
  The vastness and complexity of the biochemical networks that have been mapped out by modern genomics calls for decomposition into subnetworks. Such networks can have inherent non-local features that require the global structure to be taken into account in the decomposition procedure. Furthermore, basic questions such as to what extent the network (graph theoretically) can be said to be built by distinct subnetworks are little studied.
  
  
  RESULTS
  We present a method to decompose biochemical networks into subnetworks based on the global geometry of the network. This method enables us to analyze the full hierarchical organization of biochemical networks and is applied to 43 organisms from the WIT database. Two types of biochemical networks are considered: metabolic networks and whole-cellular networks (also including for example information processes). Conceptual and quantitative ways of describing the hierarchical ordering are discussed. The general picture of the metabolic networks arising from our study is that of a few core-clusters centred around the most highly connected substances enclosed by other substances in outer shells, and a few other well-defined subnetworks.
  
  
  BACKGROUND
  An implementation of our algorithm and other programs for analyzing the data is available from http://www.tp.umu.se/forskning/networks/meta/
  
  
  BACKGROUND
  Supplementary material is available at http://www.tp.umu.se/forskning/networks/meta/
authors.affiliation: Department of Theoretical Physics, Umea, 901 87 Umea, Sweden. holme@tp.umu.se
authors.forename: Petter
authors.initials: P
authors.lastname: Holme
authors.forename: Mikael
authors.initials: M
authors.lastname: Huss
authors.forename: Hawoong
authors.initials: H
authors.lastname: Jeong
mesh_terms: D000465:Algorithms; D023281:Genomics / Q000379:methods; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D011019:Pneumonia, Mycoplasma / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D014210:Treponema pallidum / Q000378:metabolism*