Abstract
Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic β-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.
Highlights
IntroductionVarious autoimmune disorders influence human health; type 1 diabetes (T1D), a form of diabetes mellitus in humans and animal research, is a group of metabolic disorders in which insulin-secreting β-cells are targeted by biased decisions of the immune system
To decipher complex system behaviors resulting from inter- and intra-cellular and signaling networks linking the immune system and metabolism during type 1 diabetes (T1D) progression, we developed a hybrid computational framework to unravel high computational complexity levels for individual-specific T1D development in non-obese diabetic mice
Various autoimmune disorders influence human health; type 1 diabetes (T1D), a form of diabetes mellitus in humans and animal research, is a group of metabolic disorders in which insulin-secreting β-cells are targeted by biased decisions of the immune system
Summary
Various autoimmune disorders influence human health; type 1 diabetes (T1D), a form of diabetes mellitus in humans and animal research, is a group of metabolic disorders in which insulin-secreting β-cells are targeted by biased decisions of the immune system. Various questions and problems typically arise from researchers and the disease on a daily basis that require long and tedious experimental work to possibly find an answer. Experiments alone cannot often explain the behavior of very rich and complex developmental dynamics of pancreatic islets and β-cells with feedback across different levels of biological organization as pointed out in Anmar and Santiago’s review [1]. Such a quantitative approach emerging from modeling the detailed biology of immune responses could provide novel insights into the mechanisms underlying the regulation of T1D when the safety, reproducibility, and efficiency of the present experimental techniques are challenging
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