title: A more efficient search strategy for aging genes based on connectivity.
abstract: BACKGROUND
  Many aging genes have been found from unbiased screens in model organisms. Genetic interventions promoting longevity are usually quantitative, while in many other biological fields (e.g. development) null mutations alone have been very informative. Therefore, in the case of aging the task is larger and the need for a more efficient genetic search strategy is especially strong.
  
  
  RESULTS
  The topology of genetic and metabolic networks is organized according to a scale-free distribution, in which hubs with large numbers of links are present. We have developed a computational model of aging genes as the hubs of biological networks. The computational model shows that, after generalized damage, the function of a network with scale-free topology can be significantly restored by a limited intervention on the hubs. Analyses of data on aging genes and biological networks support the applicability of the model to biological aging. The model also might explain several of the properties of aging genes, including the high degree of conservation across different species. The model suggests that aging genes tend to have a higher number of connections and therefore supports a strategy, based on connectivity, for prioritizing what might otherwise be a random search for aging genes.
authors.affiliation: The Burnham Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
authors.forename: Luca
authors.initials: L
authors.lastname: Ferrarini
authors.forename: Luca
authors.initials: L
authors.lastname: Bertelli
authors.forename: Jacob
authors.initials: J
authors.lastname: Feala
authors.forename: Andrew D
authors.initials: AD
authors.lastname: McCulloch
authors.forename: Giovanni
authors.initials: G
authors.lastname: Paternostro
mesh_terms: D000375:Aging / Q000235:genetics*; D003198:Computer Simulation; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D005828:Genetics, Population; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D015398:Signal Transduction / Q000235:genetics*