Core--Computational Modeling of Mammalian Biomolecular Responses

核心——哺乳动物生物分子反应的计算模型

基本信息

批准号：
7792425
负责人：
Rory Clement Bards Conolly
金额：
$ 22.67万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/7792425
关键词：
Adverse effects Affect Aryl Hydrocarbon Receptor Behavior Binding Biochemical Biochemical Pathway Biological Chemicals Code Complex Computer Simulation Computer software Data Databases Development Dioxins Dose Drug Kinetics Educational process of instructing Ensure Environment Equation Evaluation Experimental Designs Exposure to Goals Graph Hand Health Human Immunoglobulin M Knowledge Laboratories Lead Ligands Link Literature Maps Mathematics Mission Modeling Molecular Pathway interactions Pharmacodynamics Physiological Positioning Attribute Postdoctoral Fellow Principal Investigator Process Production Proteins PubMed Public Health Publications Reading Relative (related person)Research Research Personnel Research Project Grants Resources Risk Risk Assessment Running Safety Shapes Signal Transduction Site Specific qualifier value Spices Structure Sweden System Systems Biology Tetrachlorodibenzodioxin Tissues Training Uncertainty Visual Work base biological systems career design environmental chemical graphical user interface insight interest microbial model development network models pharmacokinetic model programs receptor research study response simulation skills software development spatial relationship spelling

项目摘要

Knowledge of the shape of the dose-response curve must extend to levels at which humans are typically exposed if we are to accurately assess the risks of adverse effects on the public health from exposures to environmental chemicals. Health effects data are usually sparse at environmental levels of exposure and computational models are being used to estimate both chemical disposition (i.e., pharmacokinetics) and tissue responses (i.e., pharmacodynamics). While current pharmacokinetic models incorporate physiological and anatomical information to provide accurate estimates of target tissue doses, the pharmacodynamic relationship between a chemical at its target site and the ultimate biological effect is usually described empirically or semi-empirically. Molecular level descriptions of pharmacodynamic mechanisms would provide a better understanding of dose-response curves and would reduce uncertainty in safety and risk assessments. The mission of this Core will be to provide the skills and resources needed to develop computational models of biochemical pathways and to thereby provide insight into the adverse health effects of TCDD and related chemicals. Since development of computational models is an iterative process, with model development and laboratory experiments proceeding hand-in-hand, the work in this Core will be collaborative with the work in the Research Projects that the Core supports. The overall approach to be used for development of computational models is defined by 4 Specific Aims: SA1. Develop initial descriptions of biochemical pathways where the nodes of the pathway and the interactions between nodes are linked to biomedical databases. SA2. Develop a directed graph by curating the pathway description obtained under SA1. SA3. Develop computational models based on the network structures described by directed graphs. SA4. Determine if a stochastic or Boolean model is preferable to an ODE-based model for understanding the dynamic behavior of a particular biochemical network. This Core will also seek to train postdoctoral fellows and other staff from the Research Projects in the use of software for development of pathway maps and for computational modeling of the pathways.

对剂量反应曲线形状的了解必须扩展到人类通常的水平如果我们要准确评估暴露于公众健康的不利影响的风险环境化学品。环境暴露水平的健康影响数据通常很少，计算模型用于估计化学处置（即药代动力学）和组织反应（即药效学）。虽然当前的药代动力学模型结合了生理学和解剖信息，以提供目标组织剂量的准确估计、药效学通常描述化学物质在其目标位点与最终生物效应之间的关系经验或半经验。药效机制的分子水平描述提供对剂量反应曲线的更好理解，并减少安全性和风险的不确定性评估。该核心的使命是提供开发所需的技能和资源生化途径的计算模型，从而深入了解不利的健康影响 TCDD 及相关化学品。由于计算模型的开发是一个迭代过程，模型开发和实验室实验齐头并进，该核心的工作将是与核心支持的研究项目的工作进行协作。这用于开发计算模型的总体方法由 4 个具体目标定义： SA1。开发生化途径的初步描述，其中途径的节点和节点之间的交互链接到生物医学数据库。 SA2。通过整理 SA1 下获得的路径描述来开发有向图。 SA3。根据有向图描述的网络结构开发计算模型。 SA4。确定随机模型或布尔模型是否比基于 ODE 的模型更适合理解特定生化网络的动态行为。该核心还将寻求培训博士后研究员和研究项目的其他工作人员如何使用用于开发路径图和路径计算模型的软件。