In contrast to the classic view of static DNA double strand breaks (DSBs) being repaired at the site of damage, we hypothesize that DSBs move and merge with each other over large distances (µm). As X-ray dose increases, the probability of having DSB clusters increases and so does the probability of misrepair and cell death. Experimental work characterizing the dose dependence of radiation-induced foci (RIF) from X-ray in nonmalignant human mammary epithelial cells (MCF10A) is used here to validate a DSB clustering model. We then use the principles of the local effect model (LEM) to predict the yield of DSB at the sub-micron level. Two mechanisms for DSB clustering are first compared: random coalescence of DSBs versus active movement of DSBs into repair domains. Simulations that best predict both RIF dose dependence and cell survival following X-ray favor the repair domain hypothesis, suggesting the nucleus is divided into an array of regularly spaced repair domains of ~1.55 µm sides. Applying the same approach to high-LET ion tracks, we can predict experimental RIF/µm along tracks with an overall relative error of 12%, for LET ranging between 30 and 350 keV/µm and for three different ions. Finally, cell death is predicted by assuming an exponential dependence on the total number of DSBs and of all possible combinations of paired DSBs within each simulated RIF. RBE predictions for cell survival of MCF10A exposed to high-LET show an LET dependence that matches previous experimental results for similar cell types. Overall, this work suggests that microdosimetric properties of ion tracks at the sub-micron level are sufficient to explain both RIF data and survival curves for any LET, similarly to the LEM assumption. On the other hand, high-LET death mechanism does not have to infer linear-quadratic dose formalism as done in the LEM. In addition, the size of repair domains derived in our model are based on experimental RIF and are three times larger than the hypothetical LEM voxel used to fit survival curves. Our model is therefore an alternative to previous approaches by providing a testable biological mechanism (i.e. RIF). More generally, DSB pairing will help develop more accurate alternatives to the simplistic linear cancer risk model (LNT) currently used for regulating exposure to very low levels of ionizing radiation.Vadhavkar et al.