1DATA Consortium, Kansas State University Olathe, Olathe, Kansas, United States of America.
Department of Mathematics, Kansas State University, Manhattan, Kansas, United States of America.
PLoS Comput Biol. 2021 Sep 27;17(9):e1009413. doi: 10.1371/journal.pcbi.1009413. eCollection 2021 Sep.
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.
1 型糖尿病(T1D)中胰腺β细胞的持续破坏是多种胰腺细胞相互作用在不同阶段进展的结果。由于耦合非线性系统的固有异质性,基于 T1D 病因的计算建模有助于系统地理解生物学过程和 T1D 健康结果。主要挑战是设计这样一个可靠的框架,根据细胞网络和生物学参数的知识来分析 T1D 的高度协调生物学。我们构建了一种新的混合计算模型,以揭示非肥胖型糖尿病小鼠模型中 T1D 的发病、进展和预防。该计算方法集成了数学建模、基于代理的建模和先进的统计方法,允许对关键生物学参数和细胞行为的时变空间网络进行建模。通过整合多种细胞类型之间的相互作用,模型结果捕获了 T1D 进展的个体特异性动态,并针对浸润性 CD8+T 细胞的数量对实验数据进行了验证。我们的模拟结果揭示了五种自身破坏性机制之间的相关性,这些机制确定了潜在治疗策略的组合:胰岛中细胞毒性 CD8+T 细胞的平均寿命;初始凋亡β细胞数量;树突状细胞(DC)的募集率;DC 上幼稚 CD8+T 细胞的结合位点;以及 DC 移动所需的时间。针对治疗的模拟结果进一步表明,所提出的治疗策略的疗效取决于治疗干预的类型和时间以及治疗剂量的给予量。我们的研究结果表明,建模自身免疫性 T1D 所依据的免疫原性以及鉴定作为潜在生物标志物的自身抗原是预测疾病发病和进展的两个紧迫参数。