Integrative Approach to Characterizing Gene Regulation

表征基因调控的综合方法

• 批准号: 7188527
• 负责人: LUIS A. Nunes AMARAL
• 金额: $ 16.07万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-02 至 2009-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7188527
• 关键词: Accounting; Affect; Agreement; Algorithms; Area; Base Pairing; Base Sequence; Behavior; Binding; Cell Nucleus; Cells; Cereals; Class; Classification; Complex; Computer software; Condition; DNA; DNA Probes; Data; Data Analyses; Databases; Depth; Development; Devices; Diagnosis; Diffusion; Disease; Disease Progression; Elements; Error Sources; Event; Excision; Exhibits; Figs - dietary; Gene Cluster; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genotype; Goals; Ions; Kinetics; Label; Lasers; Lead; Length; Light; Marketing; Measurement; Measures; Mechanics; Mediating; Medical; Methodology; Methods; Metric; Microarray Analysis; Minor; Modeling; Molecular; Nature; Noise; Nuclear Physics; Nucleic Acid Hybridization; Nucleosides; Nucleotides; Numbers; Oligonucleotide Microarrays; Oligonucleotides; Organism; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Physiological; Plant Roots; Price; Procedures; Process; Property; Proteins; Range; Rate; Reaction; Recording of previous events; Research; Research Personnel; Role; Sampling; Signal Transduction; Single-Stranded DNA; Solvents; Source; Specificity; Spottings; Statistical Study; Statistically Significant; Structure; System; Techniques; Testing; Time; Tissues; Uncertainty; Variant; Work; base; cDNA Arrays; clinical application; coping; data acquisition; design; detector; ds-DNA; gene therapy; human disease; image processing; improved; insight; interest; manufacturing process; microchip; physical property; quantum; radius bone structure; research study; retinal rods; simulation; statistics; stem; sugar; theories; tool; vector

DESCRIPTION (provided by applicant): Microarray experiments are a powerful method for the analysis of gene expression levels at a systems scale. Microarray techniques can illuminate how gene expression is modified under pathological or stressful conditions, and provide insight into the molecular mechanisms of disease. However, intra-gene spot-to-spot variability in some microarray experiments is much larger than one would expect from stability considerations, implying that a better understanding of the kinetics of nucleic acid hybridization is necessary in order to better interpret experimental results and to improve microarray design. Microarrays enable researchers to quickly obtain quantitative data for the simultaneous expression levels of thousands of genes. However, the determination of the significance of such data vis-a-vis the vast amounts of scientific information available on genes, gene products, tissues, cells and organisms, requires the application of statistical techniques. Thanks to the interest in these problems and the concerted effort of many researchers, several different techniques for data analysis are nowadays available. However, available methods often disregard the inherent uncertainties in the data and their effect on the estimation of cross correlations among expression levels of different genes. In view of these challenges, the main goals of the proposed research are: (i) to obtain a fundamental understanding of DNA hybridization in microarrays, and (ii) to develop algorithms that are able to distinguish true correlations between changes in expression levels of genes from spurious correlations that appear due to noise. To achieve the first goal, we will develop a new meso-scale model for DNA hybridization in microarrays. To achieve the second goal, we will generalize random matrix theory methods to the study of cross-correlations among changes in expression levels for different genes. An improved understanding of these questions will lead to the development of more accurate tools for the study of information exchange within gene regulation networks, enabling one to better predict the effect of perturbations to the state of a cell. Our goals also hold the potential to lead to a deeper understanding of the mechanisms that control multi-cellular development and to shed light on pathological cellular events, including the onset and progression of human disease. Moreover, our research will help to better assess the effect of drugs on patients undergoing disease progression, and ease the process of distinguishing normal, carrier and disease genotypes beforehand.

描述（由申请人提供）： 微阵列实验是在系统规模上分析基因表达水平的强大方法。微阵列技术可以阐明基因表达在病理或应激条件下如何改变，并提供对疾病分子机制的深入了解。然而，一些微阵列实验中的基因内点与点之间的变异性远大于从稳定性考虑中预期的变异性，这意味着有必要更好地了解核酸杂交的动力学，以便更好地解释实验结果并改进微阵列设计。 微阵列使研究人员能够快速获得数千个基因同时表达水平的定量数据。然而，确定此类数据相对于基因、基因产物、组织、细胞和生物体的大量科学信息的重要性，需要应用统计技术。由于对这些问题的兴趣以及许多研究人员的共同努力，现在可以使用几种不同的数据分析技术。然而，现有的方法常常忽视数据固有的不确定性及其对不同基因表达水平之间互相关性估计的影响。 鉴于这些挑战，拟议研究的主要目标是：（i）获得对微阵列中 DNA 杂交的基本了解，以及（ii）开发能够区分基因表达水平变化之间真正相关性的算法由于噪声而出现的虚假相关性。为了实现第一个目标，我们将开发一种新的微阵列 DNA 杂交介观模型。为了实现第二个目标，我们将随机矩阵理论方法推广到研究不同基因表达水平变化之间的互相关性。 对这些问题的更好的理解将有助于开发更准确的工具来研究基因调控网络内的信息交换，从而使人们能够更好地预测扰动对细胞状态的影响。我们的目标还有可能使人们更深入地了解控制多细胞发育的机制，并阐明病理细胞事件，包括人类疾病的发生和进展。此外，我们的研究将有助于更好地评估药物对疾病进展患者的影响，并简化事先区分正常基因型、携带者基因型和疾病基因型的过程。