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Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information

Author

Listed:
  • Liang Xu

    (Carnegie Institution for Science)

  • Emily Zakem

    (Carnegie Institution for Science)

  • JL Weissman

    (Stony Brook University
    Stony Brook University)

Abstract

Microbial maximum growth rates vary widely across species and are key parameters for ecosystem modeling. Measuring these rates is challenging, but genomic features like codon usage statistics provide useful signals for predicting growth rates for as-yet uncultivated organisms. Here we present Phydon, a framework for genome-based maximum growth rate prediction that combines codon statistics and phylogenetic information to enhance the precision of maximum growth rate estimates, especially when a close relative with a known growth rate is available. We use Phydon to construct a large and taxonomically broad database of temperature-corrected growth rate estimates for 111,349 microbial species. The results reveal a bimodal distribution of maximum growth rates, resolving distinct groups of fast and slow growers. Our work provides insight into the predictive power of taxonomic information versus mechanistic, gene-based inference.

Suggested Citation

  • Liang Xu & Emily Zakem & JL Weissman, 2025. "Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59558-9
    DOI: 10.1038/s41467-025-59558-9
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    References listed on IDEAS

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    1. Olga A Nev & Richard J Lindsay & Alys Jepson & Lisa Butt & Robert E Beardmore & Ivana Gudelj, 2021. "Predicting microbial growth dynamics in response to nutrient availability," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
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