Potapov, Anatolij P.

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","227"],["dc.bibliographiccitation.journal","BMC bioinformatics"],["dc.bibliographiccitation.lastpage","15"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Potapov, Anatolij P."],["dc.contributor.author","Goemann, Björn"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-10T08:12:56Z"],["dc.date.available","2019-07-10T08:12:56Z"],["dc.date.issued","2008"],["dc.description.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.Conclusion: 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."],["dc.identifier.fs","208708"],["dc.identifier.ppn","575637900"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4333"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61079"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks"],["dc.title.alternative","Methodology article"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","134"],["dc.bibliographiccitation.issue","3-4"],["dc.bibliographiccitation.journal","Network Biology"],["dc.bibliographiccitation.lastpage","148"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Goemann, Björn"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Potapov, Anatolij P."],["dc.date.accessioned","2019-07-10T08:13:56Z"],["dc.date.available","2019-07-10T08:13:56Z"],["dc.date.issued","2011"],["dc.description.abstract","We have comparatively investigated three different mammalian networks – on transcription, signal transduction and metabolic processes - with respect to their common and individual topological traits. The networks have been constructed based on genome-wide data collected from human, mouse and rat. None of these three networks exhibits a pure power-law degree distribution and, therefore, could be considered scalefree. Rather, the degree distributions of all three networks were best fitted by mixed models of a power law with an exponential tail. The networks differ from one another in the quantitative parameters of the models. Moreover, the transcription network can also be very well approximated by an exponential law. The connectivity within each network is rather robust, as is seen when removing individual nodes and computing the values of their pairwise disconnectivity index (PDI), which characterizes the topological significance of each node v by the number of direct or indirect connections in the network that critically depend on the presence of v. The results evidence that the networks are not centralized: none of nodes globally controls the integrity of each network. Just a few vertices appeared to strongly affect the coherence of the networks. These nodes are characterized by a broad range of degrees, thereby indicating that the degree alone is not the decisive criteria of a node’s importance. The networks reveal distinct architectures: The transcriptional network exhibits a hierarchical modularity, whereas the signaling network is mainly comprised of semi-autonomous modules. The metabolic network seems to be made by a more complex mixture of substructures. Thus, despite being encoded by the same genomes, the networks significantly differ from one another in their general architectural design. Altogether, our results indicate that the subsets of genes and relationships that constitute these networks have co-evolved very differently and through multiple mechanisms."],["dc.identifier.fs","585575"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8240"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61381"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","Topological peculiarities of mammalian networks with different functionalities: transcription, signal transduction and metabolic networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]