Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes

摘要

Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multiface ted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiologyhelp 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 orchestratedbiology of T1D based on the knowledge of cellular networks and biological parameters. Weconstructed a novel hybrid in-silico computational model to unravel T1D onset, progression,and prevention in a non-obese-diabetic mouse model. The computational approach thatintegrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks ofcell behaviors. By integrating interactions between multiple cell types, model results cap tured 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 thecorrelation between five auto-destructive mechanisms identifying a combination of potentialtherapeutic 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 forna?ve CD8+T-cells; and time required for DCs movement. Results from therapy-directedsimulations further suggest the efficacy of proposed therapeutic strategies depends uponthe type and time of administering therapy interventions and the administered amount oftherapeutic dose. Our findings show modeling immunogenicity that underlies autoimmuneT1D and identifying autoantigens that serve as potential biomarkers are two pressingparameters to predict disease onset and progression.