title: Structural comparison of metabolic networks in selected single cell organisms.
abstract: BACKGROUND
  There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life.
  
  
  RESULTS
  Three groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network.
  
  
  CONCLUSIONS
  If these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation.
authors.affiliation: Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu
authors.forename: Dongxiao
authors.initials: D
authors.lastname: Zhu
authors.forename: Zhaohui S
authors.initials: ZS
authors.lastname: Qin
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001105:Archaea / Q000502:physiology*; D001419:Bacteria / Q000378:metabolism; D044822:Biodiversity; D002468:Cell Physiological Phenomena; D002477:Cells; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005057:Eukaryotic Cells; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011485:Protein Binding; D025941:Protein Interaction Mapping; D020543:Proteome; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism* / Q000502:physiology*; D015398:Signal Transduction; D012984:Software; D012985:Software Design; D013045:Species Specificity; D014584:User-Computer Interface
chemical_list: D020543:Proteome