基于跳跃距离的贝叶斯分析方法揭示单分子精细输运特征
A Jump Distance-based Bayesian analysis method to unveil fine single molecule transport features
摘要：
单分子追踪（SMT）方法在细胞生物学的许多领域中得到了相当大的扩展，因为生物机制中细胞组分的动力学变得越来越相关。尽管SMT技术的发展，它仍然是难以调和的稀疏信号在任何时候（需要区分单个分子）与长的个人轨迹，在有限的区域内的细胞和给定的实验限制。这大大降低了当前数据分析方法从单分子轨迹中提取有意义的传输特征的性能。在这项工作中，我们开发并实现了一种新的SMT数据的数学分析方法，该方法利用了通常在体内用细胞系统获得的大量（短）轨迹。该方法基于跳跃距离分布的拟合，例如，表示分子在设定的时间间隔内行进多远的分布;它使用贝叶斯方法来比较合理的分子运动模型并提取定性和定量信息。最后，该方法进行了测试，在硅片上使用Monte Carlo算法模拟的轨迹，并确定该方法产生准确结果的参数范围。
英文摘要：
Single-molecule tracking (SMT) methods are under considerable expansion in many fields of cell biology, as the dynamics of cellular components in biological mechanisms becomes increasingly relevant. Despite the development of SMT technologies, it is still difficult to reconcile a sparse signal at all times (required to distinguish single molecules) with long individual trajectories, within confined regions of the cell and given experimental limitations. This strongly reduces the performance of current data analysis methods in extracting meaningful transport features from single molecules trajectories. In this work, we develop and implement a new mathematical analysis method of SMT data, which takes advantage of the large number of (short) trajectories that are typically obtained with cellular systems in vivo. The method is based on the fitting of the jump distance distribution, e.g. the distribution that represents how far molecules travel in a set time interval; it uses a Bayesian approach to compare plausible molecule motion models and extract both qualitative and quantitative information. Finally, the method is tested on in silico trajectories simulated using Monte Carlo algorithms, and ranges of parameters for which the method yields accurate results are determined.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
