Statistical Models In Toxicology And Biochemistry

本项目侧重于(1)发展将“前沿”研究成果纳入未来风险评估的方法；(2)发展设计研究的方法，以改进风险估计，特别是在涉及机械数据时；(3)开发评估暴露、剂量反应形状和效力的方法；(4)在涉及多种机制的情况下，开发评估混合物的方法；(5)制订协调癌症和非癌症健康风险评估的方法；(6)监管界通过专家小组、同行评审和合作研究直接参与；(7)通过将理论发展与精确和方便的计算方法仔细联系起来，对随机过程进行实际改进；(8)与NIEHS内的研究小组和从事类似工作的研究小组合作，以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，不断完善疾病发病率模型的生物学基础；（10）以科学可信的方式将疾病发生率模型与毒物动力学模型联系起来；（11）在模型的发展和应用中使用最广泛的数据；（12）国家毒理学计划的支持。

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.