Core--Computational Modeling of Mammalian Biomolecular R

核心--哺乳动物生物分子R的计算模型

• 批准号: 7064118
• 负责人: Rory Clement Bards Conolly
• 金额: $ 22.55万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7064118
• 关键词: aromatic hydrocarbon receptor; benzofurans; biochemistry; biological signal transduction; biomedical facility; carbopolycyclic compound; computational biology; dioxins; environmental contamination; environmental exposure; halobiphenyl /halotriphenyl compound; model design /development; molecular biology; training

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延伸到人类通常 如果我们要准确评估暴露于以下物质对公众健康产生不利影响的风险， 环境化学品。在环境接触水平上的健康影响数据通常很少， 计算模型被用于估计化学处置（即，药代动力学）和 组织反应（即，药效学）。虽然目前的药代动力学模型结合了生理 和解剖信息，以提供靶组织剂量的准确估计，药效学 通常描述化学品在其靶部位与最终生物效应之间的关系 凭经验或半凭经验。药效学机制的分子水平描述将 更好地理解剂量-反应曲线，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发展所需的技能和资源， 生物化学途径的计算模型，从而提供对不良健康影响的见解 四氯二苯并对二恶英及相关化学品的含量。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，本核心的工作将是 与核心支持的研究项目中的工作协作。的 用于开发计算模型的总体方法由4个具体目标定义： SA 1.发展生化途径的初步描述，其中途径的节点和 节点之间的交互与生物医学数据库相关联。 SA 2.通过整理在SA 1下获得的途径描述来开发有向图。 SA 3.基于有向图描述的网络结构开发计算模型。 SA 4.确定随机模型或布尔模型是否优于基于ODE的模型 特定生化网络的动态行为。 该核心还将设法培训博士后研究员和研究项目的其他工作人员使用 用于开发路径图和用于路径的计算建模的软件。