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Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas

Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas
整合计算和实验模型来预测胰腺的毒性
批准号:
10576042
负责人:
Uduak Zenas George
金额:
$18.24万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-02-15 至 2025-12-31

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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.
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