Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understa...

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Autores principales:	Silvan Türkcan, Jean-Baptiste Masson
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8776c66ad0864aedb44704fd738ec255
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spelling	oai:doaj.org-article:8776c66ad0864aedb44704fd738ec2552021-11-18T08:40:58ZBayesian decision tree for the classification of the mode of motion in single-molecule trajectories.1932-620310.1371/journal.pone.0082799https://doaj.org/article/8776c66ad0864aedb44704fd738ec2552013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376584/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens [Formula: see text]-toxin (CP[Formula: see text]T) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CP[Formula: see text]T trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments.Silvan TürkcanJean-Baptiste MassonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82799 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Silvan Türkcan Jean-Baptiste Masson Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
description	Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens [Formula: see text]-toxin (CP[Formula: see text]T) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CP[Formula: see text]T trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments.
format	article
author	Silvan Türkcan Jean-Baptiste Masson
author_facet	Silvan Türkcan Jean-Baptiste Masson
author_sort	Silvan Türkcan
title	Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
title_short	Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
title_full	Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
title_fullStr	Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
title_full_unstemmed	Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
title_sort	bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/8776c66ad0864aedb44704fd738ec255
work_keys_str_mv	AT silvanturkcan bayesiandecisiontreefortheclassificationofthemodeofmotioninsinglemoleculetrajectories AT jeanbaptistemasson bayesiandecisiontreefortheclassificationofthemodeofmotioninsinglemoleculetrajectories
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Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.

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