Theoretical Models of Single Molecule Dynamics from Minimal Photon Numbers

项目摘要 与生物医学直接相关的基本细胞内过程，如基因调控和 转录-通常涉及大的蛋白质簇动态组装和拆卸内小 在接近成像数据采集的时间尺度上的衍射限制体积。尽管令人印象深刻的μs-ms数据 通过许多SM荧光方法实现的收集时间尺度，单分子（SM）动力学参数 通常是从收集的大量数据（数百万光子）中确定的，并在 很长的时间这会损害理论上编码信息的数据的时间分辨率 关于可能发生的事件，并在MS内解决。 在ms中解析复杂过程的洞察力提出了一个深刻的分析挑战。丰达- 在心理上，这是因为高度随机的SM是由同样随机的测量间接监控的- 与SM密不可分的输出：光子。因此，我们的整体目标是发展一个 框架，以确定相关的下游复杂的细胞内过程的动力学模型， 从非常有限的数据（即，时间跟踪几十毫秒或几千个光子）。 对于这项FTRD资助，我们的重点是在简单的体外测试数据集上对我们的框架进行基准测试。 为了解决这些快速的动态，我们将依靠数据科学和统计学的尖端工具， 贝叶斯非参数（BNP）在很大程度上未知的自然科学。在这里，我们将调整BNP工具- 一些不到五年的历史，并在这里提出的第一次为自然科学应用-提供 对来自共聚焦装置（Speci fic Aim I）和单分子生物学的数据进行了全新的处理， 荧光共振能量转移称为smFRET（Speci fic Aim II）-生物学中的两种主力。作为 BNP是高度灵活的，我们制定策略，严格限制他们与知识的措施- 加工过程，例如，点扩散函数的形状。 对于这两个具体目标，我们将开发完全集成和无监督的方法来解决SM动态 通过利用BNP从ms值的数据中建模。特别是具体目标I，我们将从 从来自共焦实验的单光子到达。我们将确定扩散物种的数量 （与处理多聚体混合物有关）以及每个物种的扩散系数。通过解决 扩散系数与FCS的精度相同，仅来自数千个（而不是数百万个）光子， 我们可以收集更短的迹线，从而显著地最小化样品光损伤。或者，我们 可以使用长迹线来解决以前无法确定的量，例如，扩散系数差异 多聚体混合物。对于特定目标II，我们将确定通常从当前smFRET获得的量 分析，但现在占光谱串扰，标签闪烁，并确定分子的数量 states.解释这种光物理学深深地影响了我们对smFRET数据的最终解释。

Project Summary Fundamental intracellular processes of immediate relevance to biomedicine–such as gene regulation and transcription–often involve large clusters of proteins dynamically assembling and disassembling within small diffraction-limited volumes at timescales approaching imaging data acquisition. Despite impressive μs-ms data collection timescales achieved by many SM fluorescence methods, single molecule (SM) kinetic parameters are often instead determined from large quantities of data (millions of photons) collected and averaged over long timescales. This compromises the temporal resolution of the data that theoretically encodes information on events that may unfold and be resolved within ms. Drawing insight on complex processes resolved within ms presents a profound analysis challenge. Funda- mentally, this is because highly stochastic SMs are indirectly monitored by the equally stochastic measure- ment output to which SMs are inextricably tied: photons. Our overall objective is therefore to develop a framework to determine dynamical models–relevant downstream to complex intra-cellular processes– resolved at the SM level from very limited data (i.e., time traces tens of ms or thousand of photons). For this FTRD grant, our focus is on benchmarking our framework on simple in vitro test data sets. To resolve these fast dynamics, we will rely on cutting-edge tools from Data Science and Statistics termed Bayesian nonparametrics (BNPs) largely unknown to the Natural Sciences. Here we will adapt BNP tools– some less than five years old and proposed here for the first time for Natural Science applications–to provide a fundamentally new treatment of data derived from confocal setups (Specific Aim I) and single molecule flu- orescence resonance energy transfer termed smFRET (Specific Aim II)–both workhorses across Biology. As BNPs are highly flexible, we develop strategies to rigorously constrain them with knowledge of the measure- ment process, e.g., the shape of the point spread function. For both Specific Aims, we will develop fully-integrated and unsupervised methods to resolve SM dynamical models from ms worth of data by exploiting BNPs. In particular for Specific Aim I, we will do so starting from single photon arrivals derived from confocal experiments. We will determine diffusive species numbers (relevant in dealing with multimeric mixtures) as well as the diffusion coefficients for each species. By resolving diffusion coefficients with the same precision as FCS from just thousands (as opposed to millions) of photons, we could collect far shorter traces thereby dramatically minimizing sample photo-damage. Alternatively, we could use long traces to resolve previously indeterminable quantities, e.g., diffusion coefficient differences in multimeric mixtures. For Specific Aim II we will determine quantities normally derived from current smFRET analysis but now accounting for spectral cross-talk, label blinking and determine the number of molecular states. Accounting for such photo-physics deeply influences our ultimate interpretation of smFRET data.