2002/06/17 by Petter Holme, Mikael Huss, Hawoong Jeong · 10 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Graph Theory and Algorithms #Microbial Metabolic Engineering and Bioproduction #cond-mat.dis-nn #q-bio.MN
paper · pdf · doi:10.1093/bioinformatics/btg033
published as Bioinformatics 19, 532-538 (2003) · 9 pages, color, submitted to Bioinformatics
arxiv created 2002/06/17 · openalex publication_date 2003/02/28 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
MOTIVATION: 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. AVAILABILITY: An implementation of our algorithm and other programs for analyzing the data is available from http://www.tp.umu.se/forskning/networks/meta/ SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.tp.umu.se/forskning/networks/meta/