Statistical Models In Toxicology And Biochemistry

复杂疾病的发病机制涉及遗传和环境因素随着时间的推移的整合，使得使用传统的实验方法来梳理表型、基因型和环境因素之间的关系尤其困难。利用以基因为中心的数据库，我们通过识别与遗传和环境贡献相关的关键分子途径，开发了一个复杂疾病和环境因素的网络。与已知的化学疾病关系的比较，以及对代谢综合征和神经精神亚网络中聚集的几种环境因素和表型的基因表达数据集的转录调控分析，支持了我们的网络假设。该分析确定天然和合成类维生素a、抗精神病药物、Omega - 3脂肪酸和拟除虫菊酯农药是通过PPAR和脂肪细胞因子信号传导代谢综合征表型的潜在环境调节剂，有机磷农药是神经精神表型的潜在环境调节剂。确定整合遗传和环境调节剂的关键调控途径，确定与疾病相关的目标，这将允许对大量环境因素进行有效筛选，筛选可以为进一步研究确定优先事项并指导公共卫生决策。

Pathogenesis of complex diseases involves the integration of genetic and environmental factors over time, making it particularly difficult to tease apart relationships between phenotype, genotype, and environmental factors using traditional experimental approaches. Using gene-centered databases, we have developed a network of complex diseases and environmental factors through the identification of key molecular pathways associated with both genetic and environmental contributions. Comparison with known chemical disease relationships and analysis of transcriptional regulation from gene expression datasets for several environmental factors and phenotypes clustered in a metabolic syndrome and neuropsychiatric subnetwork supports our network hypotheses. This analysis identifies natural and synthetic retinoids, antipsychotic medications, Omega 3 fatty acids, and pyrethroid pesticides as potential environmental modulators of metabolic syndrome phenotypes through PPAR and adipocytokine signaling and organophosphate pesticides as potential environmental modulators of neuropsychiatric phenotypes. Identification of key regulatory pathways that integrate genetic and environmental modulators define disease associated targets that will allow for efficient screening of large numbers of environmental factors, screening that could set priorities for further research and guide public health decisions. With the increasing availability of molecular data collected from living systems under conditions of insult, it is imperative to focus on a smaller domain of the organism in order to understand the mechanisms by which these insults affect the organisms. Biochemical pathways are an obvious target for studying molecular data. We developed a method for finding enriched pathways relevant to a studied condition that, in addition to using the measured molecular data, would use the structural information of the pathway viewed as a network of nodes and edges. We justify the ways in which we incorporate this structural information using real biological data. We also perform extensive tests using simulated data from two networks and three genomic data sets (gene expression data from lung cancer patients, gene expression data following treatment of cyclopamine in Xenopus laevis and gene polymorphism data associated with breast cancer) and finally we compare the method to two existing approaches. The analysis provided demonstrates that not only is the method proposed very competitive with the current approaches but also provides more biologically relevant results. The National Toxicology Program is developing a high throughput screening (HTS) program to set testing priorities for compounds of interest, to identify mechanisms of action, and potentially to develop predictive models for human toxicity. This program will generate extensive data on the activity of large numbers of chemicals in a wide variety of biochemical- and cell-based assays. The first step in relating patterns of response among batteries of HTS assays to in vivo toxicity is to distinguish between positive and negative compounds in individual assays. Here, we report on a statistical approach developed to identify compounds positive or negative in a HTS cytotoxicity assay based on data collected from screening 1353 compounds for concentration-response effects in nine human and four rodent cell types. In this approach, we develop methods to normalize the data (removing bias due to the location of the compound on the 1536-well plates used in the assay) and to analyze for concentration-response relationships. Various statistical tests for identifying significant concentration-response relationships and for addressing reproducibility are developed and presented. A Markov model was developed that predicts the growth of populations of C. elegans. The model was developed using observations from a 60 h growth study in which five cohorts of 300 nematodes each were aspirated and measured every 12 h. Frequency distributions of log(EXT) measurements that are made when loading C. elegans L1 larvae into 96 well plates (t = 0 h) are used by the model to predict the frequency distributions of the same set of nematodes when measured at 12 h intervals. The model prediction coincided well with the biological observations confirming the validity of the model. The model was also applied to log(TOF) measurements following an adaptation. The adaptation accounted for variability in TOF measurements associated with potential curling or shortening of the nematodes as they pass through the flow cell of the Biosort. By providing accurate estimates of frequencies of EXT or TOF measurements following varying growth periods, the model was able to estimate growth rates. Best model fits showed that C. elegans did not grow at a constant exponential rate. Growth was best described with three different rates. Microscopic observations indicated that the points where the growth rates changed corresponded to specific developmental events: the L1/L2 molt and the start of oogenesis in young adult C. elegans. Quantitative analysis of COPAS Biosort measurements of C. elegans in growth has been hampered by the lack of a mathematical model. In addition, extraneous matter and the inability to assign specific measurements to specific nematodes made it difficult to estimate growth rates. The present model addresses these problems through a population-based Markov model. We tested the hypothesis that TCDD induced developmental neurotoxicity through an AhR dependent interaction with key regulatory neuronal differentiation pathways during telencephalon development. To test this hypothesis we examined global gene expression in both dorsal and ventral telencephalon tissues in E13.5 AhR -/- and wildtype mice exposed to TCDD or vehicle. Consistent with previous biochemical, pathological and behavioral studies, our results suggest TCDD initiated changes in gene expression in the developing telencephalon are primarily AhR dependent, as no statistically significant gene expression changes are evident after TCDD exposure in AhR -/- mice. Based on a gene regulatory network for neuronal specification in the developing telencephalon, the present analysis suggests differentiation of GABAergic neurons in the ventral forebrain is compromised in TCDD exposed and AhR-/- mice. In addition, our analysis suggests Sox11 may be directly regulated by AhR based on gene expression and comparative genomics analyses. In conclusion, this analysis supports the hypothesis that AhR has a specific role in the normal development of the forebrain and provides a mechanistic framework for neurodevelopmental toxicity of chemicals that perturb AhR signaling Human exposure to engineered nanomaterials is likely to increase dramatically in the next decade due to the rapidly developing field of nanotechnology. Over 720 products containing nanoparticles (NPs) have been put on the market including cosmetic products and drug delivery for cancer therapy. By definition, NPs refer to those materials with at least one dimension of 1 100 nm 1. The small size of NPs greatly increase their surface area per unit mass and facilitates their uptake into cells and across cells into the blood and lymph circulation to reach various target sites 2. Both of these properties render NPs potentially more reactive biologically and potentially more toxic. Evidence has shown that the NPs can cause cytotoxicity by inducing oxidative stress 3. In vivo studies showed diverse toxic effects, depending on the type of particle tested and the exposure route 4,5. In this project, we developed a physiologically-based pharmacokinetic (PBPK) model for nano and micron sized fluorescent polystyrene (PS) spheres using in vivo distribution data in rats to predict and understand the kinetics of the nanoparticles.