Mutation and Selection in Bacteria: Modelling and Calibration

Bayliss, Christopher D.; Fallaize, Christopher J.; Howitt, R.; Tretyakov, Michael V.

doi:10.1007/s11538-018-0529-9

Cited by 1 publication

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References 40 publications

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“…The empirical foundation of our method is merely a standard competition assay but one in which the superior type arises through native mutation rather than by experimental addition of known mutants ( 6 , 10 , 33 , 43 ). Our maximum likelihood estimation is suited to the simplicity of our underlying model, but the estimation could be expanded to considerable model complexity by using Approximate Bayesian Computation (ABC), albeit that the ABC approach may involve considerably more empirical analysis and computational trial and error (e.g., 2 ). We also acknowledge that similar but alternative approaches have been developed, specifically tailored to the problem of plasmid maintenance ( 4 , 5 , 22 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

Methods for measuring the evolutionary stability of engineered genomes to improve their longevity

et al. 2021

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Diverse applications rely on engineering microbes to carry and express foreign transgenes. This engineered baggage rarely benefits the microbe and is thus prone to rapid evolutionary loss when the microbe is propagated. For applications where a transgene must be maintained for extended periods of growth, slowing the rate of transgene evolution is critical and can be achieved by reducing either the rate of mutation or the strength of selection. Because the benefits realized by changing these quantities will not usually be equal, it is important to know which will yield the greatest improvement to the evolutionary half-life of the engineering. Here, we provide a method for jointly estimating the mutation rate of transgene loss and the strength of selection favoring these transgene-free, revertant individuals. The method requires data from serial transfer experiments in which the frequency of engineered genomes is monitored periodically. Simple mathematical models are developed that use these estimates to predict the half-life of the engineered transgene and provide quantitative predictions for how alterations to mutation and selection will influence longevity. The estimation method and predictive tools have been implemented as an interactive web application, MuSe.

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