title: Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks.
abstract: Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking bimodal distribution in the activity of reactions. Motivated by this, we propose a simple algorithm to decompose the metabolic network into three subnetworks. It turns out that our reaction classifier, which is blind to the biochemical role of pathways, leads to three functionally relevant subnetworks that correspond to input, output, and intermediate parts of the metabolic network with distinct structural characteristics. Our decomposition method unveils a functional bow-tie organization of metabolic networks that is different from the bow-tie structure determined by graph-theoretic methods that do not incorporate functionality.
authors.affiliation: Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India.
authors.forename: Shalini
authors.initials: S
authors.lastname: Singh
authors.forename: Areejit
authors.initials: A
authors.lastname: Samal
authors.forename: Varun
authors.initials: V
authors.lastname: Giri
authors.forename: Sandeep
authors.initials: S
authors.lastname: Krishna
authors.forename: Nandula
authors.initials: N
authors.lastname: Raghuram
authors.forename: Sanjay
authors.initials: S
authors.lastname: Jain
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D064688:Metabolic Flux Analysis / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological