ArticleNature communications2025
Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Species selection criteria for sustainable bacterial single cell protein.NPJ science of food · 2026Review
- Constructing a "periodic table" of bacteria to map diversity in trait space.The ISME journal · 2026Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Relative abundances of bacterial phyla are strong indicators of community-scale microbial growth rates in soil.Environmental microbiome · 2025Article
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Explainable rule-based prediction of cultivation media for microbes.Computational and structural biotechnology journal · 2025Article
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Authors and funding
3 authors.
Funding
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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.
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