Back

Characterizing mechanics of membrane tether from relaxation curve obtained by optical tweezers

Li, X.; Song, X.; Li, Y.; Li, M.; Wang, H.

2020-09-07 biophysics
10.1101/2020.09.06.285353 bioRxiv
Show abstract

Optical tweezers is a powerful tool in the study of membrane tension. Comparing to pulling out an entire membrane tether at one time, the step-like method is more efficient because multiple relaxation curves can be obtained from one membrane tether. However, there is few proper models that describe relaxation curves to characterize mechanical properties of cell membrane. Here we established a model to describe the relaxation curve of HeLa cells based on the relationship between membrane tether diameter and tensions. We obtained effective viscosities and static tensions by fitting relaxation curves to our model. We noticed the delicate structure of relaxation curves contains information of cell skeleton changes and protein diffusion. Our study paved a novel pathway to characterize the dynamics and mechanics of cell membrane.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Biophysical Journal
○ 631 papers in training set
Top 0.3%
19.4%
Similar papers in this journal
2
Soft Matter
● 60 papers in training set
Top 0.1%
19.4%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 13%
5.8%
Similar papers in this journal
4
Cytoskeleton
○ 27 papers in training set
Top 0.1%
3.6%
Similar papers in this journal
5
The Journal of Chemical Physics
● 56 papers in training set
Top 0.2%
3.4%
Similar papers in this journal
50% of probability mass above
6
Physical Review E
● 112 papers in training set
Top 0.4%
3.3%
7
Physical Chemistry Chemical Physics
● 36 papers in training set
Top 0.2%
2.8%
8
PLOS ONE
● 5266 papers in training set
Top 38%
2.8%
9
eLife
● 5828 papers in training set
Top 41%
2.5%
10
The Journal of Physical Chemistry B
● 167 papers in training set
Top 0.8%
2.5%
11
Physical Review Research
● 49 papers in training set
Top 0.4%
2.0%
12
The European Physical Journal Plus
○ 13 papers in training set
Top 0.1%
1.8%
13
Physical Biology
○ 46 papers in training set
Top 0.5%
1.4%
14
RSC Advances
● 22 papers in training set
Top 0.4%
1.4%
15
Biophysical Reports
○ 37 papers in training set
Top 0.3%
1.2%
16
Langmuir
● 36 papers in training set
Top 0.4%
1.1%
17
Biophysics and Physicobiology
● 11 papers in training set
Top 0.1%
1.1%
18
Biosystems
○ 31 papers in training set
Top 0.4%
1.1%
19
Biomedical Optics Express
● 95 papers in training set
Top 0.9%
0.9%
20
Communications Physics
○ 14 papers in training set
Top 0.1%
0.9%
21
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
0.9%
22
The European Physical Journal E
○ 14 papers in training set
Top 0.2%
0.9%
23
PLOS Computational Biology
● 1863 papers in training set
Top 21%
0.6%
24
Frontiers in Physics
○ 21 papers in training set
Top 0.3%
0.6%
25
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.3%
0.6%
26
Journal of Computational Neuroscience
○ 29 papers in training set
Top 0.5%
0.5%
27
Journal of Colloid and Interface Science
○ 12 papers in training set
Top 0.3%
0.5%
28
Journal of Biomechanical Engineering
● 20 papers in training set
Top 0.7%
0.5%
29
iScience
○ 1154 papers in training set
Top 44%
0.5%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.