Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications
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Abstract
Abstract Phenotypic delay – the time delay between genetic mutation and expression of the corresponding phenotype – is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data. Author Summary Understanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics - termed the phenotypic delay - and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.
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The dynamics of mutant cells can be modelled by a simple birth-and-death process.
“The dynamics of the mutant cells can be modelled by a simple birth-and-death process, as previously suggested [34,41,42,64,65].”
From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [64]
The Methods and models section (page unavailable) says the authors model mutated cells with an agent-based simulation in which cells divide. It does not describe that model as a simple birth-and-death process; a birth–death process appears only in the title of a work listed in the References. (LLM, conf=0.82)
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Sousa, M., Machado, I., Simões, L. C., & Simões, M. V. (2025) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2025.100557 · Cited by 69 · see citing works
Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial
Marrec, L., & Bitbol, A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007798 · Cited by 49 · see citing works
Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance
Ingham, J., Ruan, J.-L., & Coelho, M. A. (2025) · BJC Reports · doi:10.1038/s44276-025-00129-2 · Cited by 36 · see citing works
Łazowski, K. (2023) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2023.111816 · Cited by 10 · see citing works
Whole genome analysis and antimicrobial resistance of Neisseria gonorrhoeae isolates from Ghana
Agbodzi, B., Duodu, S., Dela, H., Kumordjie, S., Yeboah, C., Behene, E., Ocansey, K., Yanney, J. N., Boateng-Sarfo, G., Kwofie, S. K., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1163450 · Cited by 8 · see citing works
Antibiotic resistance: Insights from evolution experiments and mathematical modeling
Petrungaro, G., Mulla, Y., & Bollenbach, T. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100365 · Cited by 8 · see citing works
Model-based inference of a dual role for HOPS in regulating guard cell vacuole fusion
Hodgens, C., Flaherty, D. T., Pullen, A.-M., Khan, I., English, N. J., Gillan, L., Rojas‐Pierce, M., & Akpa, B. S. (2024) · in silico Plants · doi:10.1093/insilicoplants/diae015 · Cited by 6 · see citing works
Solving the puzzle of Fe homeostasis by integrating molecular, mathematical, and societal models
Hodgens, C., Akpa, B. S., & Long, T. A. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102149 · Cited by 1 · see citing works
Ugwuanyi, S., Thomas, G., Tietgen, H., Dreyer, F., Abbadi, A., & Snowdon, R. J. (2025) · Legume Science · doi:10.1002/leg3.70057 · Cited by 1 · see citing works
Rana, A., Patton, D., Turner, N. T., Dillon, M. M., Cooper, V. S., & Sung, W. (2021) · Genetics · doi:10.1093/genetics/iyab117 · Cited by 1 · see citing works
Population genetics: An introduction for physicists
Iglesias-Ramas, A., Lipani, S. P., & Allen, R. J. (2024) · SciPost Physics Lecture Notes · doi:10.21468/scipostphyslectnotes.89
Self-citationSoares, J. M., Calçada, C., Yakovlev, V. V., Blanco, K. C., & Bagnato, V. S. (2026) · Photochemical & Photobiological Sciences · doi:10.1007/s43630-026-00934-9
A DNA-barcoded Luria-Delbrück assay resolves mechanisms of adaptationPreprint
Husain, K. B., Flagg, L., Pincus, D., & Murugan, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.03.742544
Lilja, E., Allen, R. J., & Waclaw, B. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014666
Self-citationAli, S., Bibi, H., Mehmood, M. S., Mustufa, C., & Kamal, M. A. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02696-y
Tauzin, A., & Frénoy, A. (2026) · Evolution · doi:10.1093/evolut/qpag185
- Authors
- Carballo-Pacheco, M., Nicholson, M. D., Lilja, E. E., Allen, R. J., & Waclaw, B.
- Affiliations
- Harvard University (US)
- University of Edinburgh (GB)
- Corresponding author
- Allen, Rosalind J.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-05-29
- Volume
- 16
- Issue
- 5
- Pages
- e1007930
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC7307788
- OpenAlex
- W3032723080
- Cited by
- 18
- References
- 107
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 2 · 2021: 3 · 2020: 2
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Update notices
- new_version (2020-06-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Royal Society of Edinburgh (Personal Research Fellowship)
- European Research Council (682237)
- Engineering and Physical Sciences Research Council (PhD studenship)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Health Sciences › Medicine › Infectious Diseases › Antimicrobial Resistance in Staphylococcus
- Keywords
- bacterial antibiotic resistance, resistance evolution, Luria-Delbrück fluctuation test, mutation rate estimation, mechanistic model
- MeSH terms
- Biological Evolution, Models, Genetic, Mutation, Phenotype, Polyploidy, Drug Resistance, Bacterial
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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