Memoryless self-reinforcing directionality in endosomal active transport within living cells
Abstract
In contrast to Brownian transport, the active motility of microbes, cells, animals and even humans often follows another random process known as truncated Levy walk1,2. These stochastic motions are characterized by clustered small steps and intermittent longer jumps that often extend towards the size of the entire system. As there are repeated suggestions, although disagreement, that Levy walks have functional advantages over Brownian motion in random searching and transport kinetics3-8, their intentional engineering into active materials could be useful. Here, we show experimentally in the classic active matter system of intracellular trafficking9-15 that Brownian-like steps self-organize into truncated Levy walks through an apparent time-independent positive feedback such that directional persistence increases with the distance traveled persistently. A molecular model that allows the maximum output of the active propelling forces to fluctuate slowly fits the experiments quantitatively. Furthermore, our findings offer design principles for programming efficient transport in active materials.
- Authors:
-
- Univ. of Illinois, Urbana, IL (United States)
- Univ. of Illinois, Urbana, IL (United States); IBS Center for Soft and Living Matter, UNIST, Ulsan (South Korea)
- Publication Date:
- Research Org.:
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1252187
- Grant/Contract Number:
- FG02-02ER46019
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Materials
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 6; Journal ID: ISSN 1476-1122
- Publisher:
- Springer Nature - Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 59 BASIC BIOLOGICAL SCIENCES; 77 NANOSCIENCE AND NANOTECHNOLOGY; self-assembly
Citation Formats
Chen, Kejia, Wang, Bo, and Granick, Steve. Memoryless self-reinforcing directionality in endosomal active transport within living cells. United States: N. p., 2015.
Web. doi:10.1038/nmat4239.
Chen, Kejia, Wang, Bo, & Granick, Steve. Memoryless self-reinforcing directionality in endosomal active transport within living cells. United States. https://doi.org/10.1038/nmat4239
Chen, Kejia, Wang, Bo, and Granick, Steve. Mon .
"Memoryless self-reinforcing directionality in endosomal active transport within living cells". United States. https://doi.org/10.1038/nmat4239. https://www.osti.gov/servlets/purl/1252187.
@article{osti_1252187,
title = {Memoryless self-reinforcing directionality in endosomal active transport within living cells},
author = {Chen, Kejia and Wang, Bo and Granick, Steve},
abstractNote = {In contrast to Brownian transport, the active motility of microbes, cells, animals and even humans often follows another random process known as truncated Levy walk1,2. These stochastic motions are characterized by clustered small steps and intermittent longer jumps that often extend towards the size of the entire system. As there are repeated suggestions, although disagreement, that Levy walks have functional advantages over Brownian motion in random searching and transport kinetics3-8, their intentional engineering into active materials could be useful. Here, we show experimentally in the classic active matter system of intracellular trafficking9-15 that Brownian-like steps self-organize into truncated Levy walks through an apparent time-independent positive feedback such that directional persistence increases with the distance traveled persistently. A molecular model that allows the maximum output of the active propelling forces to fluctuate slowly fits the experiments quantitatively. Furthermore, our findings offer design principles for programming efficient transport in active materials.},
doi = {10.1038/nmat4239},
journal = {Nature Materials},
number = 6,
volume = 14,
place = {United States},
year = {Mon Mar 30 00:00:00 EDT 2015},
month = {Mon Mar 30 00:00:00 EDT 2015}
}
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