This review article describes our simplified biophysical model for the response of a group of cells to ionizing radiation. The model, which is a product of 10 years of studies, acts as (a) a comprehensive stochastic approach based on the Monte Carlo simulation with a probability tree and (b) the thereof derived detailed deterministic models describing the selected biophysical and radiobiological phenomena in an analytical manner. Specifically, the presented model describes effects such as the risk of neoplastic transformation of cells relative to the absorbed radiation dose, the dynamics of tumor development, the priming dose effect (also called the Raper–Yonezawa effect) based on the introduced adaptive response approach, and the bystander effect. The model is also modifiable depending on users’ potential needs.

The radiation adaptive response effect is a biophysical phenomenon responsible for the enhancement of repair processes in irradiated cells. This can be observed in dedicated radiobiological experiments, e.g., where the small priming dose of ionising radiation is given before the high challenging one (the so-called Raper–Yonezawa effect). The situation is more complicated when the whole complex system (the organism) is taken into consideration; many other mechanisms make the adaptive response weaker and—in some cases—practically insignificant. The recently published simplified Monte Carlo model of human lymphocytes irradiation by X-rays allows for the calculation of the level of repair enhancement by the adaptive response when every other cellular biological mechanism is implemented. The qualitative results show that the adaptive response phenomenon, observed with some probability on a basic level, usually blurs among other effects and becomes weaker than expected. Regardless, the radiation adaptive response is still an important biophysical effect which needs to be taken into consideration in low-dose radiobiological studies.

The present paper proposes a novel method, based on Bayesian statistics, as a new approach in the field of thermoluminescence dosimetry for the assessment of personal doses in mixed beta-gamma radiation fields. The method can be utilized in situations when the classical way of dose calculation is insufficient or impossible. The proposed method uses a prior function which can be assigned to the unknown parameter and the likelihood function obtained from an experiment, which together can be transformed into the posterior probability distribution of the sought parameter. Finally, the distribution is converted to the value of the dose. The proposed method is supported by analytical and Monte Carlo calculations, which confirmed the results obtained through the Bayesian approach.