TY - JOUR
T1 - What can we learn from single molecule trajectories?
AU - Ruprecht, V
AU - Axmann, M
AU - Wieser, S
AU - Schutz, GJ
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2011/12
Y1 - 2011/12
N2 - Diffusing membrane constituents are constantly exposed to a variety of forces that influence their stochastic path. Single molecule experiments allow for resolving trajectories at extremely high spatial and temporal accuracy, thereby offering insights into en route interactions of the tracer. In this review we discuss approaches to derive information about the underlying processes, based on single molecule tracking experiments. In particular, we focus on a new versatile way to analyze single molecule diffusion in the absence of a full analytical treatment. The method is based on comprehensive comparison of an experimental data set against the hypothetical outcome of multiple experiments performed on the computer. Since Monte Carlo simulations can be easily and rapidly performed even on state-of-the-art PCs, our method provides a simple way for testing various - even complicated - diffusion models. We describe the new method in detail, and show the applicability on two specific examples: firstly, kinetic rate constants can be derived for the transient interaction of mobile membrane proteins; secondly, residence time and corral size can be extracted for confined diffusion.
AB - Diffusing membrane constituents are constantly exposed to a variety of forces that influence their stochastic path. Single molecule experiments allow for resolving trajectories at extremely high spatial and temporal accuracy, thereby offering insights into en route interactions of the tracer. In this review we discuss approaches to derive information about the underlying processes, based on single molecule tracking experiments. In particular, we focus on a new versatile way to analyze single molecule diffusion in the absence of a full analytical treatment. The method is based on comprehensive comparison of an experimental data set against the hypothetical outcome of multiple experiments performed on the computer. Since Monte Carlo simulations can be easily and rapidly performed even on state-of-the-art PCs, our method provides a simple way for testing various - even complicated - diffusion models. We describe the new method in detail, and show the applicability on two specific examples: firstly, kinetic rate constants can be derived for the transient interaction of mobile membrane proteins; secondly, residence time and corral size can be extracted for confined diffusion.
KW - 2D random walk
KW - Confined diffusion
KW - Diffusion models
KW - Monte carlo simulation
KW - Single molecule diffusion analysis
KW - Single molecule microscopy
KW - Single particle tracking
KW - Statistical analysis of trajectories
KW - Algorithms
KW - Cell Membrane/chemistry
KW - Kinetics
KW - Diffusion
KW - Membrane Proteins/analysis
KW - Monte Carlo Method
UR - http://www.scopus.com/inward/record.url?scp=84857129581&partnerID=8YFLogxK
U2 - 10.2174/138920311798841753
DO - 10.2174/138920311798841753
M3 - Editorial
C2 - 22044145
VL - 12
SP - 714
EP - 724
JO - Current protein & peptide science
JF - Current protein & peptide science
IS - 8
ER -