Peroxiredoxins (Prxs) play central roles in the detoxification of reactive oxygen species. These proteins exist in multiple oligomeric forms, depending on their state of oxidation/reduction. The most common of these states are dimers and decamers, with decamers predominating under reduced conditions. Prxs have been modelled across multiple organisms using a variety of kinetic methods. However, their dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between dimers and decamers. This is due to the lack of available kinetics and theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we were able to obtain association and dissociation rate constants for the dimer-decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer-decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer-decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation state to hydrogen peroxide insult. This allowed us to mechanistically resolve a discrepancy between experimental data and kinetic simulations by showing that reduced Prx sites can be sequestered in a less active dimeric form, thus obviating the need to postulate an "inhibited" form of Prx as had been done in earlier models. Additionally, we have demonstrated that Prx decamer dissociation occurs within a time-frame relevant to peroxidase assays and other oxidation experiments and needs to be considered when working with Prx in a laboratory. Using computational modelling, we were able to to combine and organise different types of experimental data into a single framework to better understand the dynamics of these important antioxidant proteins.