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

• Main Authors: Jaberi-Douraki, M. (Author), Li, Y. (Author), Shi, Z. (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: animal; Animals; apoptosis; Article; autoantigen; Autoantigens; autoimmunity; binding site; biological model; biology; CD8+ T lymphocyte; CD8-Positive T-Lymphocytes; cell communication; Cell Communication; Computational Biology; computer model; computer simulation; Computer Simulation; cytotoxic T lymphocyte; dendritic cell; Dendritic Cells; Diabetes Mellitus, Type 1; disease exacerbation; Disease Progression; human; Humans; immunology; insulin dependent diabetes mellitus; Insulin-Secreting Cells; lymphocytic infiltration; mathematical model; Mice; Mice, Inbred NOD; Models, Immunological; mouse; nonhuman; nonobese diabetic mouse; pancreas islet beta cell; pathology; sensitivity analysis; simulation; software; Software; system analysis; Systems Analysis
• Online Access: View Fulltext in Publisher

 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. © 2021 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.