The model is based on an extensive survey of the public literature and input from an independent scientific advisory board. It reproduces key disease features including activation and expansion of autoreactive lymphocytes in the pancreatic lymph nodes (PLNs), islet infiltration and β cell loss leading to hyperglycaemia. The model uses ordinary differential and algebraic equations to represent the pancreas and PLN as well as dynamic interactions of multiple cell types (e.g. dendritic cells, macrophages, CD4+ T lymphocytes, CD8+ T lymphocytes, regulatory T cells, β cells). The simulated features

of untreated pathogenesis and disease outcomes for multiple interventions compare favourably find more with published experimental data. Thus, a mathematical model reproducing type 1 diabetes pathophysiology in the NOD mouse, validated based on accurate reproduction of results from multiple

published interventions, is available for in silico hypothesis testing. Predictive biosimulation research evaluating therapeutic strategies and underlying biological mechanisms is intended to deprioritize hypotheses that LY2835219 mouse impact disease outcome weakly and focus experimental research on hypotheses likely to provide insight into the disease and its treatment. While many therapeutic strategies have prevented or cured type 1 diabetes successfully in animal models such as the non-obese diabetic (NOD) mouse, all clinical trials to date have failed to do so in human subjects, suggesting that a more complex interpretation of the animal data may be warranted. In our previous evaluation of interventions attempting Glutathione peroxidase to modulate disease in the NOD mouse, we found several cases where disparate

responses had been observed following administration of a particular intervention [1]. Closer examination suggested that in some cases, dose, timing and treatment duration could theoretically account for discrepant efficacy observed within the NOD mouse model and/or between NOD versus human treatment results, underscoring their probable importance in identifying appropriate protocols for human clinical trials. We therefore maintain that an improved understanding of how protocol parameters impact treatment efficacy can be expected to improve fundamentally our interpretation of animal results and facilitate translational efforts. While theoretically desirable, it can be prohibitively expensive and time-consuming to optimize treatment protocols and fully explore treatment mechanisms of action in the laboratory. An alternative is to use physiologically based mathematical models to execute rapid, cost-efficient in silico analysis, resulting in testable predictions and recommendations for key corroborating experiments.