Radiation Exposure Theory Comparison of data on Mutation Frequencies of Mice

TL;DR

This paper introduces Radiation Exposure Theory (RET), a new mathematical model that predicts biological damage from irradiation by incorporating biological responses and scaling laws, successfully explaining experimental data on mutation frequencies in mice.

Contribution

RET extends the existing LDM model by including biological responses and proposes a universal scaling law that aligns well with experimental mutation data in mice.

Findings

RET accurately predicts mutation data across different dose rates.

Experimental data from mega mouse projects fit the universal scaling law.

RET outperforms simpler models like LNT in explaining biological damage.

Abstract

We propose Radiation Exposure Theory (RET), a mathematical framework to estimate biological damage caused by irradiation. This is an extension of LDM model which was proposed in the paper [Y. Manabe et al.: J. Phys. Soc. Jpn. 81, 104004(2012)]. The theory is based on physical protocol, 'a stimulus and its response'. It takes account of considerable response including mutation, cell death caused by outer stimulus, as well as biological functions such as proliferation, apoptosis and repair. By taking account of biological issues, namely a variety of preventable effects, which is characteristic feature of living object. RET can explain various data which simple LNT does not reproduce. As one of the characteristic features of RET, we propose a scaling law, namely all the data point with different dose rate irradiation are predicted to lie on the universal line if the variables, the biological damage function as a function of time development after artificial irradiation starts are converted to renormalized ones. The above scaling law can be compared with experimental data. We adopt the accumulated experiments performed by so-called mega mouse projects. It is found that their data points are converted into a universal scaling function and are in reasonably agreement with the prediction of RET.