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

基本信息

批准号：
9752604
负责人：
JEFF GELLES
金额：
$ 32.34万
依托单位：
BRANDEIS UNIVERSITY
依托单位国家：
美国
项目类别：
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/9752604
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 Structural Protein System Techniques Testing Time Work analytical method base experience experimental study improved instrumentation light microscopy macromolecule markov model microscopic imaging novel novel strategies open source protein function protein structure reconstitution single molecule statistics stoichiometry theories two-dimensional

项目摘要

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.

项目概要当前生物学的后基因组时代的一个中心问题是在分子水平上理解蛋白质和 RNA 机器执行所有细胞功能的化学和物理机制多波长单分子荧光共定位技术（“CoSMoS”；共定位）单分子光谱）方法已被广泛采用并用于阐明功能广泛的大分子机器的机制，从单个运动酶到然而，高效且准确的 CoSMoS 数据分析，尤其是大型数据分析，多维数据集仍然具有挑战性，因为 CoSMoS 数据集本身就很难分析。在需要避免的有限激发强度下观察热驱动的单分子过程光漂白本质上是噪声和随机的，因此将受益于基于优化统计理论以得出准确的结论。该应用提出了一种基于贝叶斯图像的 CoSMoS 数据分析新方法分类、贝叶斯马尔可夫链蒙特卡罗和其他基于统计的方法。目标是产生比现有方法更准确、易于扩展的分析方法大型数据集，并且即使在经验不足的用户手中也更加可靠，特别是，我们会这样做。开发算法并实现软件，以 1）充分利用两个中包含的信息：维度 CoSMoS 图像，2) 使用客观的、统计上严格的方法来计算概率每幅图像中存在给定的分子种类，3) 将动力学分析与图像分类相结合允许根据现有数据得出有关分子机制的最准确的结论，并且 4) 消除手动分析和主观参数调整会在现有分析方法中引入偏差。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该项目的具体目标是：1）增强时间无关贝叶斯点判别算法并表征其性能，2) 开发，实施并表征时间相关的联合贝叶斯判别/隐马尔可夫模型 (BD/HMM) 算法从 CoSMoS 数据导出分子机制，以及 3) 开发和分发用于贝叶斯 CoSMoS 图像分析的可用、有记录的开源软件包。