Photons have been used in radiotherapy for a number of years, and a lot of experience has been gained; experience which does not currently exist for protons. In order to apply this experience, and to optimise proton therapy, a dose conversion is applied, known as the Relative Biological Effectiveness (RBE). A constant RBE of 1.1 is in clinical use. However, a number of experimental studies have shown that RBE is not constant; depending on a number of factors, such as Linear Energy Transfer (LET), cell type, and dose etc. The RBE of 1.1 is based on a number of in vitro studies, however, within this data exists a significant variance. It has been estimated that proton RBE ranges from around 1, at the entrance, to around 2.5, at the distal edge. The value of 1.1 has been clinically accepted as a âsafeâ value, with no signs of significant under- or over-dosing. However, the open question of RBE, and the biologically extended range, can lead to potential degradation in treatment plan quality. For example, proton distal edges are not placed near organs at risk, where RBE is highest. A number of phenomenological models have been developed to encapsulate variable RBE. These models link cell survival parameters between photons and protons, with a scaling from LET. However, the models are fit with the same in vitro data used to derive RBE. The models also, by definition, give no information on underlying mechanisms of variable RBE, aside from implicitly stating that there is increased cell kill at increased LET. Noise in the data used to fit the models could explain the lack of clinical implementation. Mechanistically, it is believed that cell kill is a result of DNA damage and the efficacy of repair. In particular, the induction of DNA Double Strand Breaks (DSBs) has been identified as the toxic mechanism. By simulating the process of DSB induction and repair, mechanisms can be uncovered. This work presents results of such a methodology. The mechanisms that lead to direct and indirect DNA damage are simulated, with parameters of the mechanisms fit to experiments on DNA extracts or parameters taken from the literature. The mechanisms are applied to larger biological systems, making predictions of DNA damage at the cellular level. Prediction of DNA damage is correlated to conventional units that can be scored in proton therapy, dose and LET. This allows for the model predictions to be applied to clinically relevant cases, such as in treatment planning software. In all cases, the simulations predict an increase in yield, complexity, and density of DSBs with LET. This translates to an increase in misrepaired and residual DSBs, i.e. biological effect. The work provides mechanisms for the experimentally observed increase in cell kill with proton depth.