Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas

项目总结 美国疾病控制与预防中心对青年糖尿病的研究发现，I型糖尿病(T1D)的发病率增加了1.8% 2002-2012年间，II型糖尿病(T2D)的发病率每年以4.8%的速度增长。环境保护 青年糖尿病的决定因素(Teddy)研究将T1D和T2D的巨大负担归因于 环境污染物。由于糖尿病和代谢性疾病的患病率不断上升(特别是 在青少年中)，需要发育中胰腺毒性的计算模型。了解如何 化学结构、基因表达、靶组织细胞毒性等多种因素综合影响 胰腺健康至关重要。然而，在多个尺度上对多个因素进行综合分析会带来很大的影响 由于其内在的复杂性、高维性、不确定性和异质性而带来的挑战。多层 网络已经成为网络科学中一种新的方法论，它结合了多个网络，称为 “层”，变成一个数学对象。多层网络能够代表多个领域的多个因素 用于对它们之间的相互作用进行严格的计算分析。从而，揭示了两者之间的新关系 多尺度上的关键因素。本研究的总体目标是通过以下方式创建多层网络模型 我们可以根据化学物质来预测胰腺发育毒性的大小和机制 斑马鱼(Danio Rerio)模型的结构。目标1将建立定量结构-活性关系 (QSAR)模型预测药物和毒物暴露的毒性机制 发育中的胰腺。目标1的目标是利用多层网络和拓扑集群模型来 根据化学结构预测暴露与胰腺发育毒性的关系。 AIM 2将利用多尺度建模来使用分子创建不良结局路径(AOP)， 胰腺发育毒性的结构和病理标准。目标2的目标是描述 暴露可能扰乱胰腺发育和糖尿病早期发病机制的过程。我们 将开发一个严格的预测模型，可以用来更好地告知先验测试和预期 小分子在胰腺发育疾病背景下的结果，我们将构建一个 连接过氧化物酶体增殖物激活受体(PPAR)调制的框架(药理学和 毒理学)胰腺发育和早期功能异常。

PROJECT SUMMARY The CDC SEARCH for Diabetes in Youth study found that Type I Diabetes (T1D) incidence increased by 1.8% each year between 2002-2012, and Type II Diabetes (T2D) increased by 4.8%. The Environmental Determinants of Diabetes in the Young (TEDDY) study has attributed a substantial burden of T1D and T2D to environmental contaminants. Due to the increasing prevalence of diabetes and metabolic diseases (especially among youth), computational models for developmental pancreatic toxicity are needed. Understanding how multiple factors such as chemical structure, gene expression and target tissue cytotoxicity integrate and impact pancreatic health is vital. However, an integrated analysis of multiple factors at multiple scales poses great challenges due to the inherent complexity, high-dimensionality, uncertainty, and heterogeneity. Multilayer networks have emerged as a novel methodology in network science that combines multiple networks, called “layers”, into one mathematical object. Multilayer networks are able to represent multiple factors across multi- scales for a rigorous computational analysis of their interactions. Thereby, uncovering novel relations between key factors on a multi-scale. The overarching goal of this research is to create multilayer network models by which we can predict the magnitude and mechanisms of pancreatic developmental toxicity based on chemical structure in a zebrafish (Danio rerio) model. Aim 1 will build a Quantitative Structure-Activity Relationship (QSAR) model to predict mechanisms of toxicity resulting from pharmacological and toxicological exposures in the developing pancreas. The goal of Aim 1 is to utilize a multilayer network and topological clustering model to predict the relationship between exposures and pancreatic developmental toxicity based on chemical structure. Aim 2 will utilize multi-scale modeling to create an Adverse Outcome Pathway (AOP) using molecular, structural, and pathological criteria for pancreatic developmental toxicity. The goal of Aim 2 is to characterize the processes by which exposures may disrupt pancreas development and early diabetic pathogenesis. We will develop a rigorous predictive model that can be used to better inform a priori testing and expected outcomes of small molecules in the context of pancreatic developmental diseases, and we will construct a framework to connect peroxisome proliferator-activated receptor (PPAR) modulation (pharmacological & toxicological) with aberrant pancreatic development and early function.