title: Biological networks: comparison, conservation, and evolution via relative description length.
abstract: We describe a new approach for comparing cellular-biological networks and finding conserved regions in two or more such networks. Our distance measure is based on the description length of one network, given the description of the other one, and it is efficiently computable. We employ these distances as inputs for generating phylogenetic trees. Using KEGG's metabolic networks as our starting point, we obtained trees that are not perfect, but are very good (considering the characteristics of the inputs). Our approach enables us to identify conserved regions among more than a dozen metabolic networks, and among two protein interaction networks. These conserved regions appear to be biologically relevant, proving the viability of our approach.
authors: School of Computer Science, Tel Aviv University, Ramat Aviv, Israel.
authors: Benny
authors: B
authors: Chor
authors: Tamir
authors: T
authors: Tuller
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D019143:Evolution, Molecular; D008954:Models, Biological; D008957:Models, Genetic; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D015398:Signal Transduction
chemical_list: D011506:Proteins