Core--Computational Modeling of Mammalian Biomolecular Responses

对剂量-反应曲线形状的了解必须扩展到人类通常 如果我们要准确地评估接触以下物质对公众健康的不利影响的风险，我们就会暴露出来 环境化学品。健康影响数据通常是稀疏的环境水平的暴露和 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)。虽然目前的药代动力学模型包含了生理学 和解剖学信息，以提供目标组织剂量的准确估计，药效学 通常描述的是目标位置的化学物质与最终生物效应之间的关系 经验的或半经验的。药效学机制的分子水平描述将 更好地了解剂量-反应曲线，减少安全和风险方面的不确定性 评估。该核心的使命将是提供开发所需的技能和资源 生物化学途径的计算模型，从而提供对健康不利影响的洞察 TCDD和相关化学物质。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，这一核心的工作将是 与核心支持的研究项目中的工作协作。这个 用于开发计算模型的总体方法由4个具体目标定义： SA1.制定生化途径的初步描述，其中途径的节点和 节点之间的相互作用链接到生物医学数据库。 SA2.通过策划在SA1下获得的路径描述来开发有向图。 SA3.开发基于有向图描述的网络结构的计算模型。 SA4.确定随机模型还是布尔模型比基于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 用于开发路径图和对路径进行计算建模的软件。

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.