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Article . 2023 . Peer-reviewed
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Article . 2023
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https://dx.doi.org/10.48550/ar...
Article . 2022
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Article . 2023
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The Kinetic Theory of Mutation Rates

Authors: Lorenzo Pareschi; Giuseppe Toscani;

The Kinetic Theory of Mutation Rates

Abstract

The Luria–Delbrück mutation model is a cornerstone of evolution theory and has been mathematically formulated in a number of ways. In this paper, we illustrate how this model of mutation rates can be derived by means of classical statistical mechanics tools—in particular, by modeling the phenomenon resorting to methodologies borrowed from classical kinetic theory of rarefied gases. The aim is to construct a linear kinetic model that can reproduce the Luria–Delbrück distribution starting from the elementary interactions that qualitatively and quantitatively describe the variations in mutated cells. The kinetic description is easily adaptable to different situations and makes it possible to clearly identify the differences between the elementary variations, leading to the Luria–Delbrück, Lea–Coulson, and Kendall formulations, respectively. The kinetic approach additionally emphasizes basic principles which not only help to unify existing results but also allow for useful extensions.

Keywords

mutation rates, Probability (math.PR), Populations and Evolution (q-bio.PE), FOS: Physical sciences, Mathematical Physics (math-ph), FOS: Biological sciences, kinetic theory, Fokker–Planck equations, QA1-939, FOS: Mathematics, Luria–Delbrück distribution, Quantitative Biology - Populations and Evolution, Mathematics, Mathematical Physics, Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green
gold