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A Tutorial on the Balanced Minimum Evolution Problem

Author

Listed:
  • Catanzaro, Daniele

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Frohn, Martin

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Gascuel, Olivier
  • Pesenti, Raffaele

Abstract

The Balanced Minimum Evolution Problem (BMEP) is an [APX]-hard network design problem that consists of finding a minimum length unrooted binary tree (also called a phylogeny) having as a leaf-set a given set of molecular sequences. The optimal solution to the BMEP (i.e., the optimal phylogeny) encodes the hierarchical evolutionary relationships of the input sequences. This information is crucial for a multitude of research fields, ranging from systematics to medical research, passing through drug discovery, epidemiology, ecology, biodiversity assessment and population dynamics. In this article, we introduce the reader to the problem and present the current state-of-the-art; we include the most important achievements reached so far and the challenges that still remain to be addressed.

Suggested Citation

  • Catanzaro, Daniele & Frohn, Martin & Gascuel, Olivier & Pesenti, Raffaele, 2021. "A Tutorial on the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 20210, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:20210
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    References listed on IDEAS

    as
    1. Catanzaro, Daniele & Aringhieri, Roberto & Di Summa, Marco & Pesenti, Raffaele, 2015. "A branch-price-and-cut algorithm for the minimum evolution problem," European Journal of Operational Research, Elsevier, vol. 244(3), pages 753-765.
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    7. Daniele Catanzaro & Raffaele Pesenti, 2019. "Enumerating vertices of the balanced minimum evolution polytope," LIDAM Reprints CORE 3005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Daniele Catanzaro & Martine Labbé & Raffaele Pesenti & Juan-José Salazar-González, 2012. "The Balanced Minimum Evolution Problem," INFORMS Journal on Computing, INFORMS, vol. 24(2), pages 276-294, May.
    9. Daniele CATANZARO & Stanley E. SCHACKNEY & Alejandro A. SCHÄFFER & Russell SCHWARTZ, 2016. "Classifying the progression of Ductal Carcinoma from single-cell sampled data via integer linear programming: a case study," LIDAM Reprints CORE 2778, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Daniele CATANZARO & Roberto ARINGHIERI & Mardo DI SUMMA & Raffaele PESENTI, 2015. "A branch-price-and-cut algorithm for the minimum evolution problem," LIDAM Reprints CORE 2767, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    12. Catanzaro, Daniele & Frohn, Martin & Pesenti, Raffaele, 2020. "An information theory perspective on the balanced minimum evolution problem," LIDAM Reprints CORE 3097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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