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Extracting Coevolving Characters from a Tree of Species

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Discrete and Topological Models in Molecular Biology

Part of the book series: Natural Computing Series ((NCS))

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Abstract

Phylogenetic proximity has guided our understanding of the evolution of species for decades. It is clear nowadays that the paradigm “phylogenetically close species should share similar characters” is just one facet of the complex process of evolution inherent in development and species differentiation. Today, there is a need for novel mathematical approaches to cluster together symbolic information organized into trees of characters that could highlight the evolutionary relations between characters and the processes of coevolution of characters. We propose a combinatorial method to do so and to derive groups of characters which appear to be correlated through their evolutionary history. This approach was first developed for protein sequences, but it is revealed to be general and applicable to any list of characters describing species. In particular, one does not need to know all characters for all species to perform coevolution analysis.

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Author information

Authors and Affiliations

  1. Laboratoire Génomique des Microorganismes, UMR7238, Université Pierre et Marie Curie et CNRS, Paris, France

    Alessandra Carbone

  2. Department of Computer Science, Université Pierre et Marie Curie, Paris, France

    Alessandra Carbone

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  1. Alessandra Carbone
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Correspondence to Alessandra Carbone .

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Editors and Affiliations

  1. Dept. Mathematics & Statistics, University of South Florida, Tampa, Florida, USA

    Nataša Jonoska

  2. Dept. Mathematics & Statistics, University of South Florida, Tampa, Florida, USA

    Masahico Saito

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Carbone, A. (2014). Extracting Coevolving Characters from a Tree of Species. 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_3

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  • DOI: https://doi.org/10.1007/978-3-642-40193-0_3

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  • Online ISBN: 978-3-642-40193-0

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