title: Decompositions of large-scale biological systems based on dynamical properties.
abstract: BACKGROUND
  Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties.
  
  
  RESULTS
  The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems.
  
  
  BACKGROUND
  Original heuristics for the methods investigated are described in the article.
  
  
  BACKGROUND
  altafini@sissa.it
authors.affiliation: CRS4 Bioinformatica, Loc. Piscina Manna, 09010 Pula (CA), Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Soranzo
authors.forename: Fahimeh
authors.initials: F
authors.lastname: Ramezani
authors.forename: Giovanni
authors.initials: G
authors.lastname: Iacono
authors.forename: Claudio
authors.initials: C
authors.lastname: Altafini
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics / Q000378:metabolism; D049490:Systems Biology / Q000379:methods*