New statistical tools for single molecule experiments

描述(申请人提供)：单分子(SM)测量在世界各地的研究实验室中正迅速变得司空见惯，并正在为许多研究领域做出贡献，因为它们能够洞察以前因整体测量中存在的整体平均而难以解决的现象。特别是，构象非均相体系的动力学正受益于单分子研究。蛋白质折叠和构象动力学、酶学、核酶功能、细菌捕光和蛋白质-核酸相互作用只是从SM技术的应用中受益的几个复杂系统的例子。然而，由于缺乏统一的数据分析和解释，SM结果的影响有所减轻。拟议的研究集中在SM荧光测量以及如何将实验设计、分析和预期建立在坚实的统计和理论基础上。提出了三个具体目标：1.用信息论确定SM实验的基本界限。2.提出了基于隐马尔可夫模型的统计严谨分析方法。 3.将方法实现为对通用数据分析包的面向用户的添加。 这项研究对健康的意义在于它对许多正在进行的对生物系统的SM调查做出了贡献。SM测量正在彻底改变我们解决化学生物学中许多问题的方法，然而它们仍然是基于仅适用于大宗样品的整体测量的假设来解释和设计的。这可能会导致使用传统方法无法充分解释的数据收集。一个一致的SM测量理论框架将是该领域向前迈出的重要一步。 目标1将提供一个可用于实验设计的理论框架，因为它限制了测量对系统特性作出推断的能力。它还将提供判断数据简化方法的基准(Cramr-Rao界限)。Aim 2开发了算法和核心代码，以实现统计上严格的数据分析方法，从而实现系统参数的无偏估计，精度接近Cramr-Rao界限，包括意义不确定性估计。AIM 3为实验设计和分析提供了可用的工具，使其他研究人员能够利用这些方法进行自己的研究。

DESCRIPTION (provided by applicant): Single molecule (SM) measurements are rapidly becoming commonplace in research laboratories around the world and are contributing to many areas of investigation because of their ability to provide insight into phenomena that were previously intractable because of the ensemble averaging present in bulk measurements. In particular the dynamics of conformationally heterogeneous systems are benefiting from single-molecule studies. Protein folding and conformational dynamics, enzymology, ribozyme function, bacterial light harvesting, and protein-nucleic acid interactions are just a few examples of complex systems that have benefited from the application of SM techniques. However, the impact of SM results has been mitigated by the lack of uniform data analysis and interpretation. The proposed research focuses on SM fluorescence measurements and how to place the experimental design, analysis, and expectations onto solid statistical and theoretical ground. Three specific aims are proposed: 1. Use information theory to determine the fundamental limits of SM experiments. 2. Develop statistically rigorous analysis methods based on hidden Markov models. 3. Implement methods as user-oriented additions to common data analysis packages. The significance to health of this research is through its contribution to the many ongoing SM investigations into biological systems. SM measurements are revolutionizing our approach to many problems in chemical biology, yet they are still being interpreted and designed based on assumptions that are only valid for ensemble measurements of bulk samples. This can result in collection of data that cannot be adequately interpreted using traditional methods. A consistent theoretical framework for SM measurements would be a significant step forward for the field. Aim 1 will provide a theoretical framework that can be used for experimental design as it provides the limit of the measurement's ability to make inferences about the properties of the system. It will also provide the benchmark (the Cramr-Rao bound) by which to judge data reduction methods. Aim 2 develops the algorithms and core codes to implement statistically rigorous methods of data analysis that allow unbiased estimation of system parameters with accuracy approaching the Cramr-Rao bound including meaning uncertainty estimates. Aim 3 provides useable tools for experimental design and analysis to allow other investigators to exploit these methods for their own research.