title: A network-based method for target selection in metabolic networks.
abstract: BACKGROUND
  The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets.
  
  
  RESULTS
  Here, we propose an approach that is based on the mapping of the interactions between biochemical agents, such as proteins and metabolites, onto complex networks. We report that nodes and links in complex biochemical networks can be grouped into a small number of classes, based on their role in connecting different functional modules. Specifically, for metabolic networks, in which nodes represent metabolites and links represent enzymes, we demonstrate that some enzyme classes are more likely to be essential, some are more likely to be species-specific and some are likely to be both essential and specific. Our network-based enzyme classification scheme is thus a promising tool for the identification of drug targets.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Northwestern Institute on Complex Systems and Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA. rguimera@northwestern.edu
authors.forename: R
authors.initials: R
authors.lastname: Guimerà
authors.forename: M
authors.initials: M
authors.lastname: Sales-Pardo
authors.forename: L A N
authors.initials: LA
authors.lastname: Amaral
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D001426:Bacterial Proteins / Q000378:metabolism*; D016503:Drug Delivery Systems / Q000379:methods*; D015195:Drug Design; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D001426:Bacterial Proteins