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; 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.

了解剂量响应曲线的形状必须扩展到人类通常是的水平 如果我们要准确评估对公共卫生的不利影响的风险，则暴露于公共卫生 环境化学品。健康效应数据通常在环境水平的暴露水平和 计算模型被用来估计化学处置（即药代动力学）和 组织反应（即药效学）。当前的药代动力学模型融合了生理 和解剖信息以提供靶组织剂量的准确估计值 通常描述其目标部位的化学物质与最终的生物学作用之间的关系 经验或半经验。药效机制的分子水平描述 对剂量反应曲线有更好的了解，并将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，从而提供对不良健康影响的洞察力 TCDD和相关化学品的。由于计算模型的开发是一个迭代过程，因此 模型开发和实验室实验携手进行，该核心的工作将是 与核心支持的研究项目中的工作合作。这 四个特定目的定义了用于开发计算模型的总体方法： SA1。开发生化途径的初始描述，其中途径的节点和 节点之间的相互作用与生物医学数据库有关。 SA2。通过策划在SA1下获得的途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。确定随机模型是否比基于ODE的模型更可取 特定生化网络的动态行为。 该核心还将寻求从研究项目中培训博士后研究员和其他工作人员 用于开发途径图和途径计算建模的软件。