A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis
A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis
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DOI：
10.1016/j.bpj.2016.01.018
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发表时间：
2016-03-29
影响因子：
3.4
通讯作者：
Masson, Jean-Baptiste
中科院分区：
文献类型：
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作者：
El Beheiry, Mohamed;Tuerkcan, Silvan;Masson, Jean-Baptiste
跟踪活细胞中的单个分子可以提供有关其环境和运动背后的相互作用的宝贵信息。新的实验技术现在允许记录大量的个人轨迹，使先进的统计工具的数据分析的实施。在这篇文章中，我们提出了一种贝叶斯方法来处理这些数据，我们讨论了如何有效地利用它来推断单分子轨迹的物理和生化参数。
Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion. New experimental techniques now permit the recording of large amounts of individual trajectories, enabling the implementation of advanced statistical tools for data analysis. In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories.