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

描述(申请人提供)：DNA微阵列由于其高度平行的性质，原则上非常适合于快速鉴定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的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的分析和理论挑战的阻碍[7，10]。特别是，要探索的核酸序列空间可能非常大[6]，许多物种的遗传序列非常相似，不同物种存在的浓度在样本收集时通常是未知的[11]，这可能导致复杂的重叠杂交模式。为了提高识别微阵列的诊断准确性，已经提出了几种分析方法和实验设计。然而，关于特定方法的优点，文献中有很多不同的意见，因为它们已经在不同的实验平台上用不同复杂性的样本进行了测试。分析方法的实验验证是有限的，作为一般策略是不可行的[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[7，6，8，2]中。例如，最近发现的迄今隐藏的微生物多样性[1，2]使医学界认识到定植于人体的微生物群落与健康、疾病和疾病易感性之间的关系。人们越来越多地认识到某些疾病是由多个微生物引起的，例如牙周病，而不是归因于单一的致病因素[4]。这可能是解释几种迄今知之甚少的疾病(如慢性疾病[5])的原因的关键。然而，人类相关微生物区系的特征受到可用于快速微生物鉴定的适当技术的限制。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极限。为了验证模型，我们将从一个新的微流控微阵列成像平台(由Stahl和Yager Group合作开发)收集热杂交和解离数据。主要的项目目标将通过以下具体目标来实现：1)开发一个有限元的微阵列杂交数学模型，该模型捕捉我们平台的基本特征(三维凝胶单元中的竞争结合和解离、扩散和对流流动、靶长度、浓度和温度的影响)；2)使用集成的微流体平台收集新的热杂交和解离数据以验证目标1中的模型；3)使用该模型生成模拟数据集，并评估所选分析算法在对应于不同复杂程度的样本的数据集上的性能。 DNA微阵列是一项令人兴奋的新技术，用于基因筛查和疾病诊断。然而，它们还没有充分发挥其临床潜力，以评估与复杂的细菌混合物相关的健康和疾病状态。这项提议通过开发新的工具来指导数据分析和提高DNA微阵列的诊断准确性，从而解决了这一尚未开发的潜力。

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.