[en] Biological effects of ionizing radiation have been studied intensely since the start of the 20^th century, however, technological advancement and improvement in experimental techniques in molecular biology motivated the discovery of some peculiar biological phenomena only observable in interaction of organisms with low intensity ionizing radiation. This is one of the most difficult topics in radiobiology to be dealt with and its comprehension is challenging, so taking advantage of improved computational capacity and application of mathematical modeling and computer simulation of diverse biological phenomena is beneficial to the area. Such combination of advancement is promising, for better understanding of effects caused by low intensity ionizing radiation via determination of their molecular mechanisms. These effects appear after a period of latency which can extend for decades, and the probabilistic nature of these events hinders the accumulation of sufficient epidemiological data for statistical significance. In these conditions, mathematical modeling can be a helpful complementary tool. In this master thesis two mathematical models and their stochastic versions are presented, in order to understand the role of random fluctuations in the survival process of healthy cells when these are exposed to low intensity ionizing radiation. In these models, a cell population transits among four states, and in one of the models the repair of DNA damaged by ionizing radiation is processed only internally by the damaged cell itself, and in the other model the repair is done by other cells which can be located near the damaged cell or even distantly. The stochastic versions were proposed using the Langevin approach and the solutions of the models, deterministic and stochastic, were realized numerically. The simulation of stochastic version of intracellular repair model resulted in a survival dynamics different from the deterministic model; density of the healthy cells was kept for a period significantly longer in the stochastic model. (author)