Theoretical Models of Single Molecule Dynamics from Minimal Photon Numbers

最小光子数的单分子动力学理论模型

• 批准号: 10244940
• 负责人: Steve Presse
• 金额: $ 29.2万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10244940
• 关键词: 5 year old; Accounting; Benchmarking; Biology; Blinking; Code; Complex; Confocal Microscopy; Data; Data Collection; Data Reporting; Data Science; Data Set; Development; Diffuse; Diffusion; Energy Transfer; Event; Fluorescence; Gene Expression Regulation; Genetic Transcription; Grain; Grant; Head; Image; Kinetics; Knowledge; Label; Learning; Licensing; Mathematics; Measurement; Methods; Modeling; Molecular; Monitor; Morphologic artifacts; Natural Sciences; Noise; Output; Paper; Photons; Physics; Process; Proteins; Psyche structure; Publications; Sampling; Scanning; Series; Shapes; Spectrum Analysis; Technology; Theoretical model; Time; data acquisition; data exchange; data tools; experimental study; flexibility; flu; imaging approach; in vitro testing; insight; kinetic model; novel; physical science; simulation; single molecule; statistics; temporal measurement; tool

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.

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--通常涉及一大群蛋白质在小分子中动态组装和分解 在接近成像数据采集的时间尺度上的衍射限制体积。尽管μS的数据令人印象深刻 多种SMfl荧光方法获得的收集时间刻度、单分子(SM)动力学参数 通常根据收集的大量数据(数百万个光子)并对其进行平均来确定 很长的时间尺度。这损害了理论上对信息进行编码的数据的时间分辨率 关于可能在微软内部展开和解决的事件。 洞察微软内部解决的复杂流程是一项深刻的分析挑战。Funda- 在心理上，这是因为高度随机的短信被同样随机的措施间接监控- 短信与之有着千丝万缕联系的部分输出：光子。因此，我们的整体目标是发展一套 确定动力学模型的框架-与复杂的细胞内过程相关的下游- 从非常有限的数据(即，时间跟踪数十毫秒或数千个光子)在SM级别进行解析。 对于这笔FTRD赠款，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。 为了解决这些快速变化，我们将依靠来自数据科学和统计的尖端工具 贝叶斯非参数计量学(BNPS)在自然科学中几乎是未知的。在这里，我们将调整BNP工具- 有些还不到fiVe岁，在这里为fi首次提出自然科学应用-提供 对来自共聚焦装置(SPECIfic Aim I)和单分子flu的数据进行了全新的处理。 雌核共振能量转移称为SmFRET(specific Aim II)--这两种生物都是主力。AS BNP是高度fl可扩展的，我们制定了战略，通过了解该措施来严格限制它们- 分段过程，例如，点扩展函数的形状。 对于这两个特殊的fic目标，我们将开发完全集成的和无监督的方法来求解SM动态 通过利用BNPS从毫秒级的数据中进行建模。特别是对于特定的fic目标i，我们将从 来自共聚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ficients。通过解决 从几千个(而不是几百万个)光子的扩散系数获得与FCS相同的精度的fi科学， 我们可以收集更短的痕迹，从而极大地减少样本照片的损坏。或者，我们 可以使用长踪迹来解决以前无法确定的数量，例如，扩散系数fi在 多聚体混合物。对于SPECIfic Aim II，我们将确定通常从当前smFRET派生的量 分析，但现在占光谱串扰，标签闪烁和确定分子的数量 各州。解释这种深入fl中的光物理现象，将有助于我们对SMFRET数据的终极解释。