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When and How the Perfect Phylogeny Model Explains Evolution

Part of the Natural Computing Series book series (NCS)

Abstract

Character-based parsimony models have been among the most studied notions in computational evolution, but research in the field stagnated until some important, recent applications, such as the analysis of data from protein domains, protein networks, and genetic markers, as well as haplotyping, brought new life into this sector. The focus of this survey is to present the perfect phylogeny model and some of its generalizations. In particular, we develop the use of persistency in the perfect phylogeny model as a new promising computational approach to analyzing and reconstructing evolution. We show that, in this setting, some graph-theoretical notions can provide a characterization of the relationships between characters (or attributes), playing a crucial role in developing algorithmic solutions to the problem of reconstructing a maximum parsimony tree.

Keywords

  • Parsimony Model
  • Steiner Tree
  • Input Matrix
  • Chordal Graph
  • Recurrent Mutation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

PB and APC are supported by the Fondo di Ateneo 2011 grant “Metodi algoritmici per l’analisi di strutture combinatorie in bioinformatica”. GDV is supported by the Fondo di Ateneo 2011 grant “Tecniche algoritmiche avanzate in Biologia Computazionale”. PB, GDV and RD are supported by the MIUR PRIN 2010–2011 grant “Automi e Linguaggi Formali: Aspetti Matematici e Applicativi”, code H41J12000190001. TMP is supported by the Intramural Research Program of the National Institutes of Health, National Library of Medicine.

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Bonizzoni, P., Carrieri, A.P., Vedova, G.D., Dondi, R., Przytycka, T.M. (2014). When and How the Perfect Phylogeny Model Explains Evolution. In: Jonoska, N., Saito, M. (eds) Discrete and Topological Models in Molecular Biology. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40193-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-40193-0_4

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