ArticleNature communications2018
Predicting the evolution of Escherichia coli by a data-driven approach.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Toward a Functional Genetics of Adaptation: Insights From Microbial Experimental Evolution.Genome biology and evolution · 2026Review
- Using TraDIS as a complementary approach to long term evolution for mapping adaptive mutations in Escherichia coli.PLoS genetics · 2026Article
- Adaptive laboratory evolution ofFrontiers in microbiology · 2026Article
- Advances in adaptive laboratory evolution applications forSynthetic and systems biotechnology · 2025Review
- Proteome partitioning constraints in long-term laboratory evolution.Nature communications · 2024Article
- Comparative genomics hints at dispensability of multiple essential genes in two Escherichia coli L-form strains.Bioscience reports · 2023Article
- Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs.Nature communications · 2023Article
- Towards evolutionary predictions: Current promises and challenges.Evolutionary applications · 2023Review
- Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes.Current opinion in genetics & development · 2022Review
- Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.PLoS computational biology · 2022Article
- Global coordination of the mutation and growth rates across the genetic and nutritional variety inFrontiers in microbiology · 2022Article
- Article
- Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas protegens.PLoS genetics · 2021Article
- Biocide-Induced Emergence of Antibiotic Resistance inFrontiers in microbiology · 2021Article
- Review
- Predicting the decision making chemicals used for bacterial growth.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.