Joint Bayesian analysis of single-molecule colocalization images and kinetics

单分子共定位图像和动力学的联合贝叶斯分析

• 批准号: 9923002
• 负责人: JEFF GELLES
• 金额: $ 32.34万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9923002
• 关键词: Adopted; Algorithms; Bayesian Analysis; Bayesian Method; Binding; Biochemical; Biochemistry; Bioinformatics; Biological Process; Biology; Cell Extracts; Cell physiology; Cells; Chemicals; Classification; Communicable Diseases; Complex; Computer software; Cryoelectron Microscopy; DNA; Data; Data Analyses; Data Set; Development; Discrimination; Disease; Enzymes; Fluorescence; Fluorescence Microscopy; Fluorescent Dyes; Gene Expression Regulation; Genomics; Goals; Health; Human; Human Biology; Image; Image Analysis; Individual; Joints; Kinetics; Label; Laboratories; Manuals; Markov Chains; Messenger RNA; Metabolic Diseases; Methods; Microscope; Modeling; Molecular; Molecular Machines; Motor; Nucleic Acids; Organism; Outcome; Pathway interactions; Performance; Pharmaceutical Preparations; Photobleaching; Photons; Probability; Process; Proteins; Public Health; RNA; RNA Splicing; Reaction; Research; Resolution; Ribosomes; Series; Spectrum Analysis; Spliceosomes; Spottings; System; Techniques; Testing; Time; Work; analytical method; base; experience; experimental study; improved; instrumentation; large datasets; light microscopy; macromolecule; markov model; microscopic imaging; multidimensional data; novel; novel strategies; open source; protein function; protein structure; reconstitution; single molecule; statistics; stoichiometry; theories; two-dimensional

Project Summary A central concern of the present post-genomic era of biology is understanding at the molecular level the chemical and physical mechanisms by which the protein and RNA machines that perform all cellular functions operate. Multi-wavelength single-molecule fluorescence co-localization techniques (“CoSMoS”; co-localization single-molecule spectroscopy) methods have been widely adopted and used to elucidate the functional mechanisms of a broad range of macromolecular machines ranging from individual motor enzymes to the ribosome and spliceosome. However, efficient and accurate CoSMoS data analysis, particularly of large, multi-dimensional datasets, remains challenging. CoSMoS datasets are inherently difficult to analyze because observations of thermally-driven single-molecule processes at the limited excitation intensities needed to avoid photobleaching are intrinsically noisy and stochastic and thus would benefit from objective methods based on optimized statistical theory to derive accurate conclusions. This application proposes a new approach to CoSMoS data analysis based on Bayesian image classification, Bayesian Markov chain Monte Carlo, and other statistics-based methods. The overall project goal is to produce analytical methods that are more accurate than existing approaches, readily scalable to large datasets, and are more reliable, even in the hands of less experienced users. In particular, we will develop algorithms and implement software that will 1) make full use of the information contained in the two- dimensional CoSMoS images, 2) use objective, statistically rigorous approaches to calculate the probability of a given molecular species being present in each image, 3) integrate kinetic analysis with image classification to allow the most accurate conclusions about molecular mechanisms based on available data, and 4) eliminate the manual analysis and subjective parameter tweaking that introduce bias in existing analytical methods. The developed models and algorithms will be refined and validated through thorough testing against a broad range of simulated and known-outcome empirical data sets. The specific aims of the project are to: 1) enhance the Time-Independent Bayesian Spot Discrimination algorithm and characterize its performance, 2) develop, implement and characterize a time-dependent Joint Bayesian Discrimination/Hidden Markov Modeling (BD/HMM) algorithm to derive molecular mechanisms from CoSMoS data, and 3) develop and distribute a usable, documented, open-source software package for Bayesian CoSMoS image analysis.

项目摘要 目前生物学后时代的一个核心关注点是在分子层面的理解 化学和物理机制，蛋白质和RNA机器执行所有细胞功能 操作。多波长的单分子荧光共定位技术（“宇宙”；共定位 单分子光谱法）方法已被广泛采用并用于阐明功能 从单个运动酶到单个运动酶的大量大分子机制 核糖体和剪接体。但是，有效而准确的宇宙数据分析，尤其是大型的， 多维数据集仍然挑战。宇宙数据集本质上很难分析，因为 观察热驱动的单分子过程，以避免有限的兴奋强度 光漂白是本质上的噪音和随机的，因此将受益于基于客观方法 优化的统计理论得出了准确的结论。 该应用程序提出了一种基于贝叶斯图像的新方法来进行宇宙数据分析 分类，贝叶斯马尔可夫链蒙特卡洛和其他基于统计的方法。总体项目 目标是产生比现有方法更准确的分析方法，很容易扩展到 大型数据集，即使在经验不足的用户手中，也更可靠。特别是，我们会 开发算法和实施软件1）充分利用两种信息中包含的信息 维宇宙图像，2）使用目标，统计上严格的方法来计算 每个图像中存在一个给定的分子物种，3）将动力学分析与图像分类整合到 允许基于可用数据的分子机制得出最准确的结论，4）消除 在现有分析方法中引入偏见的手动分析和主观参数调整。这 开发的模型和算法将通过针对广泛范围的彻底测试进行完善和验证 模拟和已知结果的经验数据集。该项目的具体目的是：1）增强 与时间无关的贝叶斯斑点歧视算法并表征其性能，2）开发， 实施并表征与时间有关的联合贝叶斯歧视/隐藏的马尔可夫建模 （BD/HMM）算法从宇宙数据中得出分子机制，3）开发和分布 可用，记录的开源软件包，用于贝叶斯宇宙图像分析。