Statistical Models In Toxicology And Biochemistry

毒理学和生物化学的统计模型

• 批准号: 7007183
• 负责人: Christopher J Portier
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 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/7007183
• 关键词: biochemistry; cancer risk; cell cycle; chemical carcinogen; chemical carcinogenesis; dioxins; endocrinology; environmental contamination; environmental exposure; environmental toxicology; gene environment interaction; gene expression; growth /development; immunology; laboratory rat; mathematical model; model design /development; neurology; pharmacology; statistics /biometry; thyroid hormones; vital statistics

This Project focuses on (1) The development of methodology for incorporating "cutting-edge" research findings into future risk assessments; (2) The development of methods for designing studies to improve risk estimates, especially when mechanistic data is involved; (3) The development of methods for the evaluation of exposure, dose-response shape and potency; (4) The development of methods for evaluating mixtures when multiple mechanisms are involved; (5) The development of methods which harmonize cancer and non-cancer health risk assessments; (6) Direct engagement of the regulatory community through expert panels, peer review and collaborative research; (7) Practical improvement of stochastic processes through careful linkage of theoretical developments with computational methods that are accurate and convenient; (8) Collaboration with research groups within the NIEHS and research groups doing similar work to improve the biological understanding of disease incidence; (9) Iterative improvement of the biological basis for disease incidence models through a process of hypothesis testing and laboratory research; (10) Linkage of disease incidence models to toxicokinetics models in a scientifically credible manner; (11) Use of the broadest array of data in both the development of the model and its application; (12) Support of the National Toxicology Program. We developed a quantitative, statistically sound methodology for the analysis of suspected gene regulatory networks using gene expression data sets. The method is based on Bayesian networks and provides a means to directly quantify gene-expression networks and test hypotheses regarding the linkages between genes in this network. Simulation studies were performed to evaluate the behavior of this method for small samples and to address the design of future studies aimed at quantifying gene-interaction networks. Using gene expression changes in HPL1A lung airway epithelial cells after exposure to TCDD at levels of 0.1, 1.0 and 10.0 nM for 24 hours, a hypothesized gene expression network was analyzed. The method supports the assumed network and allowed the evaluation of a hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system (see Research Theme 2.A below). One of the major unresolved issues in the analysis of gene expression data is the identification of gene regulatory networks. Several methods have been proposed by others for identifying gene regulatory networks, but these methods focus on the use of multiple pairwise comparisons to identify the network structure. We developed a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods, this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the data. Bayesian methods (as above) can then be used to quantify the linkages between genes to provide a complete characterization of the resulting gene-expression network. Simulation studies were performed to evaluate the operating characteristics of the method and to determine the probabilities of finding the correct network under different design strategies. This method was applied to data on G(1)/S activation in mouse fetal fibrosis MF129 cells. The resulting gene-interaction network was used to identify the nodal genes and quantify the relationships among genes within the network. Searches for common transcription factors were used to validate the resulting networks. We have also developed computer software to enable researchers to use our Bayesian networks analysis method to analyze gene expression data. A user-friendly, windows-based format was used to make it easy for researchers to choose options for the analysis, define network structures and evaluate the resulting analysis.

该项目的重点是：（1）制定将“尖端”研究结果纳入未来风险评估的方法;（2）制定设计研究的方法，以改进风险估计，特别是在涉及机械数据时;（3）制定评估照射、剂量-反应形状和效力的方法;（4）制定涉及多种机制的混合物的评价方法;（5）制定协调癌症和非癌症健康风险评估的方法;（6）通过专家小组、同行审查和合作研究，让监管机构直接参与;（7）通过将理论发展与准确和方便的计算方法仔细联系起来，实际改进随机过程;（8）与NIEHS内的研究小组和从事类似工作的研究小组合作，提高对疾病发病率的生物学认识;（9）通过假设检验和实验室研究，不断改进疾病发病率模型的生物学基础;（10）以科学上可信的方式将疾病发病率模型与毒理学模型联系起来;（11）在模型的开发及其应用中使用最广泛的数据;（12）支持国家毒理学方案。 我们开发了一种定量的，统计学上合理的方法，用于分析可疑的基因调控网络，使用基因表达数据集。该方法是基于贝叶斯网络，并提供了一种手段，直接量化基因表达网络和测试假设的基因之间的联系，在这个网络。进行模拟研究，以评估这种方法的小样本的行为，并解决未来的研究，旨在量化基因相互作用网络的设计。利用HPL 1A肺气道上皮细胞在暴露于0.1、1.0和10.0 nM水平的TCDD 24小时后的基因表达变化，分析了一个假设的基因表达网络。该方法支持假设的网络，并允许评估一个假设，连接通常的二恶英表达变化的视黄酸受体系统（见下文的研究主题2.A）。 基因表达数据分析中的一个主要未解决的问题是基因调控网络的识别。其他人已经提出了几种方法来识别基因调控网络，但这些方法集中在使用多个成对比较来识别网络结构。我们开发了一种分析基因表达数据的方法，以确定与观察到的一组表达谱一致的调控结构。与其他方法不同，该方法通过使用基于似然的统计方法来获得与数据最一致的网络，从而超越了成对评估。贝叶斯方法（如上所述）可用于量化基因之间的联系，以提供所得基因表达网络的完整表征。进行了仿真研究，以评估该方法的操作特性，并确定在不同的设计策略下找到正确的网络的概率。将该方法应用于小鼠胎儿纤维化MF 129细胞中G（1）/S活化的数据。由此产生的基因相互作用网络被用来识别节点基因和量化网络内基因之间的关系。常见转录因子的测序用于验证所得到的网络。 我们还开发了计算机软件，使研究人员能够使用我们的贝叶斯网络分析方法来分析基因表达数据。使用了一种用户友好的基于Windows的格式，使研究人员能够轻松地选择分析选项，定义网络结构并评估分析结果。