Development of a computational model for improved diagnostic accuracy of DNA micr

开发计算模型以提高 DNA 显微镜的诊断准确性

• 批准号: 7360793
• 负责人: DAVID A STAHL
• 金额: $ 7.14万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2010-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7360793
• 关键词: Accounting; Address; Agreement; Algorithms; Awareness; Bacteria; Base Sequence; Binding; Biological Assay; Censuses; Clinical; Collaborations; Communities; Competitive Binding; Complex; Complex Mixtures; Computer Simulation; DNA; DNA Microarray Chip; Data; Data Analyses; Data Collection; Data Set; Detection; Development; Diagnostic; Diffusion; Disease; Dissociation; Drug Formulations; Elements; Employment; Etiology; Experimental Designs; Gel; Genetic; Genetic Screening; Genome; Genotype; Goals; Health; Heterogeneity; Human; Human body; Image; Kinetics; Laboratories; Length; Literature; Medical; Methods; Microarray Analysis; Microfluidics; Modeling; Molecular; Monitor; Nature; Nucleic Acids; Nucleic acid sequencing; Pattern; Performance; Periodontal Diseases; Phase; Predisposition; Sampling; Simulate; System; Technology; Temperature; Testing; Time; Validation; analytical tool; base; clinical application; clinical practice; design; diagnostic accuracy; disease diagnosis; improved; instrument; mathematical model; micro-total analysis system; microbial; microbial community; new technology; novel; real world application; research study; sample collection; simulation; tool

DESCRIPTION (provided by applicant): DNA microarrays, due to their highly parallel nature, are in principle well suited for rapid identification of known or related microbial species, but our ability to extract meaningful information from microarray images is still at a rudimentary level. The use of DNA microarrays is currently hampered by a few key analytical and theoretical challenges [7, 10]. In particular, the nucleic acid sequence space to be explored can be very large [6], the genetic sequences of many species are very similar, and the concentrations at which the different species are present is typically not known at the time of the sample collection [11], which can result in complex overlapping hybridization patterns. Several analysis methods and experimental designs have been proposed to increase the diagnostic accuracy of identification microarrays. However, there is much disagreement in the literature regarding the merits of particular approaches, as they have been tested on different experimental platforms with samples of varying complexity. Experimental validation of analysis methods is limited, and not feasible as a general strategy [6]. Advances in microarray data analysis would accelerate the employment of the powerful DNA microarray technology, already integrated into lab-on-a-chip instruments [7, 6, 8, 2], in routine clinical practice. For example, the recent discovery of hitherto hidden microbial diversity [1, 2] has led the medical community to recognize the relationship between the microbial communities colonizing the human body and health, disease, and predisposition to disease. There is an increasing awareness of a polymicrobial cause for some diseases, e.g. periodontal disease, rather than attribution to a single causative agent [4]. This could hold the key for explaining the etiology of several hitherto poorly understood diseases (e.g. Chron's disease [5]). However, the characterization of human-associated microbiota is limited by the availability of suitable technologies for rapid microbial identification. We propose to improve the diagnostic accuracy of microarrays and characterize their detection limits by utilizing computational microarray modeling as a tool for design and validation of microarray data analysis methods. For model validation, we will collect thermal hybridization and dissociation data from a novel microfluidic microarray imaging platform (developed in collaboration between Stahl and Yager group). The primary project goal will be achieved through the following specific aims: 1) development of a finite element mathematical model of microarray hybridization that captures the essential features of our platform (competitive binding and dissociation in three-dimensional gel elements, diffusion and convective flow, effects of target length, concentration, and temperature); 2) collecting novel thermal hybridization and dissociation data using the integrated microfluidic platform to validate the model in Aim 1; 3) using the model to generate simulated datasets, and assess the performance of selected analysis algorithms on datasets corresponding to samples of differing complexity.Narrative DNA microarrays are an exciting new technology for genetic screening and diagnosis of disease. However, they have yet to achieve their full clinical potential for evaluating health and disease states associated with complex mixtures of bacteria. This proposal addresses this untapped potential by developing new tools to guide data analysis and improve the diagnostic accuracy of DNA microarrays.

描述（由申请人提供）：DNA微阵列，由于其高度并行的性质，原则上非常适合于快速识别已知或相关的微生物物种，但我们从微阵列图像中提取有意义信息的能力仍处于初级水平。DNA微阵列的使用目前受到一些关键分析和理论挑战的阻碍[7,10]。特别是待探索的核酸序列空间可能非常大[6]，许多物种的基因序列非常相似，而不同物种存在的浓度在采集样本时通常是未知的[11]，这可能导致复杂的重叠杂交模式。提出了几种分析方法和实验设计，以提高识别微阵列的诊断准确性。然而，关于特定方法的优点，文献中存在很多分歧，因为它们已经在不同的实验平台上进行了不同复杂性的样本测试。分析方法的实验验证是有限的，并且不可行作为一个通用策略[6]。微阵列数据分析的进步将加速强大的DNA微阵列技术在常规临床实践中的应用，该技术已经集成到芯片实验室仪器中[7,6,8,2]。例如，最近发现了迄今为止隐藏的微生物多样性[1,2]，这使得医学界认识到在人体中定植的微生物群落与健康、疾病和疾病易感性之间的关系。人们越来越认识到某些疾病（如牙周病）是由多种微生物引起的，而不是由单一病原体引起的。这可能是解释一些迄今为止知之甚少的疾病（如慢性疾病b[5]）病因的关键。然而，人类相关微生物群的表征受到快速微生物鉴定合适技术的限制。我们建议通过利用计算微阵列建模作为设计和验证微阵列数据分析方法的工具来提高微阵列的诊断准确性并表征其检测限。为了验证模型，我们将从新型微流体微阵列成像平台（由Stahl和Yager团队合作开发）收集热杂交和解离数据。主要项目目标将通过以下具体目标实现：1)开发微阵列杂交的有限元数学模型，该模型捕获了我们平台的基本特征（三维凝胶元素的竞争性结合和解离，扩散和对流，目标长度，浓度和温度的影响）；2)利用集成微流控平台收集新的热杂交和解离数据，验证Aim 1中的模型；3)利用该模型生成模拟数据集，并评估所选分析算法在不同复杂度样本对应的数据集上的性能。叙述