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
基本信息
- 批准号:10576042
- 负责人:
- 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
- 起止时间:2023-02-15 至 2025-12-31
- 项目状态:未结题
- 来源:
- 关键词:BiologicalChemical StructureChemicalsChildhood diabetesComplexComputer AnalysisComputer ModelsCross-Sectional StudiesData SetDevelopmentDiabetes MellitusDietDiseaseEmbryoEnvironmental PollutantsEnvironmental ScienceEtiologyExperimental ModelsExposure toGene ExpressionGene TargetingGoalsHabitsHealthHeterogeneityImpairmentIncidenceIndividualInsulin-Dependent Diabetes MellitusLife Cycle StagesLife StyleMathematicsMetabolic DiseasesMethodologyMethodsModelingMolecularMorphologyNon-Insulin-Dependent Diabetes MellitusOrganOrganogenesisOutcomePPAR alphaPancreasPathogenesisPathologicPathway interactionsPeroxisome Proliferator-Activated ReceptorsPrevalenceProcessQuantitative Structure-Activity RelationshipReceptor SignalingResearchResolutionSEARCH for Diabetes in YouthScienceSignal TransductionStructureTechniquesTestingTissuesToxic effectToxicologyTranslationsUncertaintyUnhealthy DietUnited StatesYouthZebrafishadverse outcomeantagonistbiological systemschemical propertycomputational toxicologycytotoxicitydevelopmental diseasedevelopmental toxicitydevelopmental toxicologydiabeticdiet and exercisedietaryhigh dimensionalitymulti-scale modelingnetwork modelsnovelpancreas developmentpharmacologicphysical sciencepredictive modelingresponsesmall moleculespatiotemporaltranscriptomicstranslational impact
项目摘要
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.
项目总结
项目成果
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