Repeated exposure to a drug of abuse causes stable changes in the reward circuitry of the brain, which will further lead to behavioral abnormalities such as dependence, sensitization and craving. Understanding the molecular biology of drug addiction will provide improved therapies to treat drug addiction and prevent the epidemic of new addicts. It has been shown that gene expression changes in brain contribute to the stable regulation of the brain's reward circuitry involved in drug addiction. However, the specific genes and the transcriptional mechanisms underlying such regulation remain poorly understood. The overall goal of this application is to provide deeper insights into the molecular mechanisms of drug addiction by integrated analysis of rich biological data sets related to drug addiction. A large amount of genome-wide molecular profiling datasets have been accumulated to study drug addiction. These large scale data provide great opportunities to generate significant scientific findings, but also great challenges for data analysis. Studies from other research areas, especially cancer research, have shown that integrating the various molecular profiling datasets effectively can not only increase the power of data analysis, but also give more comprehensive knowledge of the biological process. The central hypothesis of this study is that integrated analysis of drug addiction related molecular profiling datasets will lead to a systematic understanding of the molecular mechanisms and novel findings of important genes and pathways involved in drug addiction. This study will focus on cocaine addiction because cocaine is among the most prominent illicit drugs of abuse, and considerable molecular profiling data have been accumulated in cocaine addiction. The proposed methods, however, will be general and applicable to study other drugs of addiction. In summary, this project will provide: (1) better mechanistic understanding of cocaine addiction;(2) powerful statistical/computational tools for the integrated analysis of drug addiction data;and (3) a comprehensive database for strengthening the broader knowledge infrastructure for drug addiction research.
Understanding the molecular biology of drug addiction will provide improved therapies to treat drug addiction and prevent the epidemic of new addicts. This project will provide better mechanistic understanding of cocaine addiction, and a comprehensive database for strengthening the broader knowledge infrastructure for drug addiction research.
|Hoshiyama, Hirotoshi; Tang, Jun; Batten, Kimberly et al. (2012) Development of methods for quantitative comparison of pooled shRNAs by mass sequencing. J Biomol Screen 17:258-65|
|Allen, Jeffrey D; Xie, Yang; Chen, Min et al. (2012) Comparing statistical methods for constructing large scale gene networks. PLoS One 7:e29348|
|Chen, Min; Xie, Yang; Story, Michael (2011) An Exponential-Gamma Convolution Model for Background Correction of Illumina BeadArray Data. Commun Stat Theory Methods 40:3055-3069|
|Xie, Yang; Xiao, Guanghua; Coombes, Kevin R et al. (2011) Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res 17:5705-14|
|Wang, Siling; Wang, Yuhang; Xie, Yang et al. (2011) A novel approach to DNA copy number data segmentation. J Bioinform Comput Biol 9:131-48|
|Chen, Min; Wang, Xinlei (2011) Approximate Predictive Densities and Their Applications in Generalized Linear Models. Comput Stat Data Anal 55:1570-1580|
|Xiao, Guanghua; Wang, Xinlei; Khodursky, Arkady B (2011) Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data. J Am Stat Assoc 106:61-72|
|Xie, Yang; Pan, Wei; Jeong, Kyeong S et al. (2010) A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data. Stat Med 29:489-503|
|Xie, Yang; Ahn, Chul (2010) Statistical methods for integrating multiple types of high-throughput data. Methods Mol Biol 620:511-29|
|LaPlant, Quincey; Vialou, Vincent; Covington 3rd, Herbert E et al. (2010) Dnmt3a regulates emotional behavior and spine plasticity in the nucleus accumbens. Nat Neurosci 13:1137-43|
Showing the most recent 10 out of 11 publications