ArticleGenetics2015
Survival probability of beneficial mutations in bacterial batch culture.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 35 citations in OpenAlex.
- Bacterial Persistence Modulates the Speed, Magnitude, and Onset of Antibiotic Resistance Evolution.Molecular biology and evolution · 2026Article
- Article
- Article
- Evolution of Microbial Growth Traits Under Serial Dilution.Genetics · 2020Article
- The Effect of Population Bottleneck Size and Selective Regime on Genetic Diversity and Evolvability in Bacteria.Genome biology and evolution · 2019Article
- Predicting microbial growth in a mixed culture from growth curve data.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Article
- Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection.Proceedings. Biological sciences · 2018Article
- Article
- A Model for Designing Adaptive Laboratory Evolution Experiments.Applied and environmental microbiology · 2017Article
- Benefits of a Recombination-Proficient Escherichia coli System for Adaptive Laboratory Evolution.Applied and environmental microbiology · 2016Article
- Exploring genetic suppression interactions on a global scale.Science (New York, N.Y.) · 2016Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The survival of rare beneficial mutations can be extremely sensitive to the organism's life history and the trait affected by the mutation. Given the tremendous impact of bacteria in batch culture as a model system for the study of adaptation, it is important to understand the survival probability of beneficial mutations in these populations. Here we develop a life-history model for bacterial populations in batch culture and predict the survival of mutations that increase fitness through their effects on specific traits: lag time, fission time, viability, and the timing of stationary phase. We find that if beneficial mutations are present in the founding population at the beginning of culture growth, mutations that reduce the mortality of daughter cells are the most likely to survive drift. In contrast, of mutations that occur de novo during growth, those that delay the onset of stationary phase are the most likely to survive. Our model predicts that approximately fivefold population growth between bottlenecks will optimize the occurrence and survival of beneficial mutations of all four types. This prediction is relatively insensitive to other model parameters, such as the lag time, fission time, or mortality rate of the population. We further estimate that bottlenecks that are more severe than this optimal prediction substantially reduce the occurrence and survival of adaptive mutations.
Indexed as
Identifiers
What OpenQuestion holds
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.