Integrative Approach to Characterizing Gene Regulation

表征基因调控的综合方法

• 批准号: 7010353
• 负责人: LUIS A. Nunes AMARAL
• 金额: $ 15.73万
• 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/7010353
• 关键词: DNA; chemical kinetics; gene expression; genetic regulation; genotype; mathematics; microarray technology; nucleic acid hybridization; nucleotides

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量表上分析基因表达水平的强大方法。微阵列技术可以阐明在病理或压力条件下基因表达如何修饰，并洞悉疾病的分子机制。然而，在某些微阵列实验中，基因内的斑点变异性要比稳定性考虑因素所期望的要大得多，这意味着必须更好地理解核酸杂交的动力学，以更好地解释实验结果并改善微阵列设计。 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。但是，确定此类数据的重要性，相对于基因，基因产物，组织，细胞和生物的大量科学信息，需要应用统计技术。由于对这些问题的兴趣和许多研究人员的共同努力，如今可以使用几种用于数据分析的技术。但是，可用的方法通常会忽略数据中固有的不确定性及其对不同基因表达水平之间跨相关性估计的影响。 鉴于这些挑战，拟议的研究的主要目标是：（i）获得对微阵列中DNA杂交的基本了解，以及（ii）开发算法，这些算法能够区分基因表达水平的真正相关性与由于噪声所致的虚假相关性的影响。为了实现第一个目标，我们将开发一个新的中尺度模型，用于微阵列中的DNA杂交。为了实现第二个目标，我们将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。 对这些问题的深入了解将导致开发更准确的工具，以研究基因调节网络中信息交换的研究，从而使人们能够更好地预测扰动对细胞状态的影响。我们的目标还具有更深入地了解控制多细胞发展的机制，并阐明病理细胞事件（包括人类疾病的发作和进展）的潜力。此外，我们的研究将有助于更好地评估药物对发生疾病进展的患者的影响，并事先简化正常，载体和疾病基因型的过程。