期刊
BIOPHYSICAL JOURNAL
卷 110, 期 6, 页码 1209-1215出版社
CELL PRESS
DOI: 10.1016/j.bpj.2016.01.018
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资金
- state program Investissements d'avenir [ANR-10-BINF-05, ANR-10-INSB-04]
- Institut Curie International PhD Program, Paris-Science-Lettres [ANR-10-IDEX-0001-02 PSL]
- ANR Grant SYNAPTUNE
- Region Ile de France Nanosciences Competence Center
- Delegation Generale de l'Armement
- RTRA Triangle de la Physique
- Bayer Science and Education Foundation
- ERC advanced research grant PlasltInhib
- program Investissements d'Avenir (MEMO LIFE) [ANR-10-LABX-54]
- program Investissements d'Avenir (PSL Research University) [ANR-11-IDEX-0001-02]
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.
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