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Algorithms for the quantitative Lock/Key model of cytoplasmic incompatibility.
Calamoneri, Tiziana; Gastaldello, Mattia; Mary, Arnaud; Sagot, Marie-France; Sinaimeri, Blerina.
Afiliação
  • Calamoneri T; Department of Computer Science, Sapienza University of Rome, viale Regina Elena 295, 00161 Rome, Italy.
  • Gastaldello M; Inria Grenoble, 655, Avenue de l'Europe, 38334 Montbonnot, France.
  • Mary A; Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, 43, Boulevard du 11 Novembre 1918, 69622 Villeurbanne, France.
  • Sagot MF; Department of Computer Science, Sapienza University of Rome, viale Regina Elena 295, 00161 Rome, Italy.
  • Sinaimeri B; Inria Grenoble, 655, Avenue de l'Europe, 38334 Montbonnot, France.
Algorithms Mol Biol ; 15: 14, 2020.
Article em En | MEDLINE | ID: mdl-32704304
ABSTRACT
Cytoplasmic incompatibility (CI) relates to the manipulation by the parasite Wolbachia of its host reproduction. Despite its widespread occurrence, the molecular basis of CI remains unclear and theoretical models have been proposed to understand the phenomenon. We consider in this paper the quantitative Lock-Key model which currently represents a good hypothesis that is consistent with the data available. CI is in this case modelled as the problem of covering the edges of a bipartite graph with the minimum number of chain subgraphs. This problem is already known to be NP-hard, and we provide an exponential algorithm with a non trivial complexity. It is frequent that depending on the dataset, there may be many optimal solutions which can be biologically quite different among them. To rely on a single optimal solution may therefore be problematic. To this purpose, we address the problem of enumerating (listing) all minimal chain subgraph covers of a bipartite graph and show that it can be solved in quasi-polynomial time. Interestingly, in order to solve the above problems, we considered also the problem of enumerating all the maximal chain subgraphs of a bipartite graph and improved on the current results in the literature for the latter. Finally, to demonstrate the usefulness of our methods we show an application on a real dataset.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Algorithms Mol Biol Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Algorithms Mol Biol Ano de publicação: 2020 Tipo de documento: Article