ArticlePLoS computational biology2026
Simple birth-death-mutation models predict some-but not all-aspects of the experimental evolution of antibiotic resistance.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Mathematical modelling of antibiotic resistance plays an important role in understanding the mechanisms of resistance emergence and spreading, testing the feasibility of new treatment protocols, and antimicrobial stewardship. However, many assumptions underlying some of the most commonly used mathematical models have not been rigorously tested experimentally. We verify whether one of these models - a birth-death-mutation process - is able to quantitatively predict the outcome of laboratory experiments. We grow bacteria in a bioreactor in conditions that closely resemble the assumptions of the model, and compare the model predictions with experimental observables such as the probability and time to resistance evolution, mutant number distribution, and the genetic composition of the evolved populations. We show that the model fails to reproduce some aspects of the experiments (failing differently for different antibiotics) but that simple modifications of the model significantly improve its predictive power. These modifications give insight into the population dynamics of resistant mutants for each antibiotic tested, and highlight the importance of quantitative modelling for accurate prediction of antibiotic resistance evolution.
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