Importantly, simulated LipCl2MDP depletes inflammatory DCs and tolerogenic DCs with equal potency, with sustained protection arising through the dynamic regulation of these DC subsets under conditions of reduced inflammation. The up-regulation of tolerogenic DCs also contribute to the simulated anti-CD3 mediated efficacy in diabetic NOD mice [102], which is again characterized by the return of an apparently benign cellular infiltrate Ponatinib [103]. In the case of anti-CD3, other mechanisms (e.g. induction of regulatory T cells) also contribute to sustained remission. The decision to represent a tolerogenic

DC phenotype illustrates how the broader immunology state-of-knowledge was brought to bear in reconciling NOD mouse results with LDE225 the reported underlying biology. Conversely, it illustrates a gap in understanding based on available NOD mouse data and an area where additional data on NOD DCs could clarify the mechanistic underpinnings of these therapies. By selecting internal validation experiments that targeted

different biological components, the virtual mouse was fine-tuned along multiple biological axes, yielding a single parameterization that reproduces a wide array of behaviours. By itself, this was a non-trivial and insightful exercise. Furthermore, external validation experiments were selected to assess the virtual mouse response to distinct stimuli, thereby indicating whether fine-tuning is a necessary prerequisite in the simulation of an appropriate response. The virtual mouse reproduced outcomes accurately for 21 of 24 experiments, representing five interventions. This generally positive result suggests that the virtual mouse could be a valuable Exoribonuclease counterpart to experimental investigations into novel therapeutic strategies (assuming the main mechanisms of action are within the scope of the modelled biology). The mismatches highlighted disparities in the published anti-CD40L data set that we had not appreciated previously. However, the potential importance of dose and

timing to outcomes, which were observed in the simulations, is entirely consistent with their importance in the experimental data, as highlighted in our 2004 review [1]. The model could, plausibly, be used to design experiments to reconcile disparate data. Additionally, dose/timing sensitivity argues that research efforts should use virtual mice whose disease progression (e.g. timing of diabetes onset) is aligned with the experimental mice and should evaluate a range of doses/timing to account for variability inherent in the data (i.e. NOD mouse colonies with variability in rate of disease progression) used to generate the model. While this model is intended to broadly support research efforts in the field of type 1 diabetes, like any other model it has limitations.