title: The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks.
abstract: BACKGROUND
  Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter--the pairwise disconnectivity index of a network's element - that is capable of such bridging.
  
  
  RESULTS
  The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes), an edge (i.e., reactions, interactions), as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness) of this network to the presence (absence) of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network.
  
  
  CONCLUSIONS
  Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations) from a network. The greatest potential value of this approach is its ability to systematically analyze the role of every element, as well as groups of elements, in a regulatory network.
authors.affiliation: Department of Bioinformatics, Medical School, Georg August University of Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany. anatolij.potapov@bioinf.med.uni-goettingen.de
authors.forename: Anatolij P
authors.initials: AP
authors.lastname: Potapov
authors.forename: Björn
authors.initials: B
authors.lastname: Goemann
authors.forename: Edgar
authors.initials: E
authors.lastname: Wingender
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome