Abstract
We propose an agent-based model for the simulation of the autoimmune response in T1D. The model incorporates cell behavior from various rules derived from the current literature and is implemented on a high-performance computing system, which enables the simulation of a significant portion of the islets in the mouse pancreas. Simulation results indicate that the model is able to capture the trends that emerge during the progression of the autoimmunity. The multi-scale nature of the model enables definition of rules or equations that govern cellular or sub-cellular level phenomena and observation of the outcomes at the tissue scale. It is expected that such a model would facilitate in vivo clinical studies through rapid testing of hypotheses and planning of future experiments by providing insight into disease progression at different scales, some of which may not be obtained easily in clinical studies. Furthermore, the modular structure of the model simplifies tasks such as the addition of new cell types, and the definition or modification of different behaviors of the environment and the cells with ease.
Highlights
Type 1 diabetes (T1D) is an autoimmune disease, in which the insulin-producing Beta cells in the pancreas are destroyed by the immune system, typically leading to complete insulin deficiency [1]
The agent-based model we present considers non-obese diabetic (NOD) mouse insulitis in a tissue section composed of islets of varying sizes, CD8+ T cells with naive, effector and memory functions, and Beta cell regeneration
This suggests that therapies that focus on strengthening the basement membrane of the islets has the potential to lead to a smaller population of T cells in the pancreatic tissue, which can be exploited for T1D treatment
Summary
Type 1 diabetes (T1D) is an autoimmune disease, in which the insulin-producing Beta cells in the pancreas are destroyed by the immune system, typically leading to complete insulin deficiency [1]. T1D is considered to constitute 5–10% of all cases of diabetes [2], its incidence was reported to have increased significantly in the past few decades [3], especially in children under five [4]. While there has been continuous efforts toward the elucidation of the biological mechanisms involved in disease pathogenesis and the optimization of treatment options, the required resources and time for the clinical testing limit the number of studies. Computational modeling is a powerful tool for assessing the feasibility of potential interventions and therapies, as well as hypothesis testing.
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