A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis

被引：21

作者：

El Beheiry, Mohamed ^{[1
,2
,6
]}

Tuerkcan, Silvan ^{[3
]}

Richly, Maximilian U. ^{[4
]}

Triller, Antoine ^{[5
]}

Alexandrou, Antigone ^{[4
]}

Dahan, Maxime ^{[1
,2
,7
]}

Masson, Jean-Baptiste ^{[6
,7
]}

机构：

[1] PSL Res Univ, Inst Curie, Lab Physicochim, Paris, France

[2] Univ Paris 04, Dept Radiat Oncol, Paris, France

[3] Stanford Univ, Sch Med, Div Med Phys, Palo Alto, CA 94304 USA

[4] Univ Paris Saclay, Ecole Polytech, Lab Opt & Biosci, Palaiseau, France

[5] PSL Res Univ, Ecole Normale Super, Biol Cellulaire Synapse, Paris, France

[6] Inst Pasteur, Phys Biol Syst, Paris, France

[7] Howard Hughes Med Inst, Janelia Res Campus, Ashburn, VA USA

来源：

BIOPHYSICAL JOURNAL | 2016年 / 110卷 / 06期

关键词：

STOCHASTIC-PROCESSES; LIVING CELLS; INFERENCE; DIFFUSION; DYNAMICS; RECEPTORS; MEMBRANE; GEPHYRIN; PROTEINS; SYSTEMS;

D O I：

10.1016/j.bpj.2016.01.018

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

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.

引用

页码：1209 / 1215

页数：7

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