Back

Ripening and synchronization of biomolecular condensates in a heterogeneous elastic medium

Meng, L.; Mao, S.; Lin, J.

2023-05-28 biophysics
10.1101/2023.05.27.542561 bioRxiv
Show abstract

Biomolecular condensates play a crucial role in regulating gene expression, but their behavior in chromatin remains poorly understood. Classical theories of phase separation are limited to thermal equilibrium, and traditional methods can only simulate a limited number of condensates. In this paper, we introduce a novel mean-field-like method that allows us to simulate millions of condensates in a heterogeneous elastic medium to model the dynamics of transcriptional condensates in chromatin. Using this method, we unveil an elastic ripening process in which the average condensate radius exhibits a unique temporal scaling, [<]R[>] [~] t1/5, different from the classical Ostwald ripening, and we theoretically derive the exponent based on energy conservation and scale invariance. We also introduce active dissolution to model the degradation of transcriptional condensates upon RNA accumulation. Surprisingly, three different kinetics of condensate growth emerge, corresponding to constitutively expressed, transcriptional-bursting, and silenced genes. Notably, multiple distributions of transcriptional-bursting kinetics from simulations, e.g., the burst frequency, agree with transcriptome-wide experimental data. Furthermore, the timing of growth initiation can be synchronized among bursting condensates, with power-law scaling between the synchronization period and dissolution rate. Our results shed light on the complex interplay between biomolecular condensates and the elastic medium, with important implications for gene expression regulation.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
PRX Life
34 papers in training set
Top 0.1%
9.9%
2
Biophysical Journal
545 papers in training set
Top 0.7%
8.2%
3
Physical Review Letters
43 papers in training set
Top 0.1%
8.1%
4
Physical Review Research
46 papers in training set
Top 0.1%
6.3%
5
Physical Review X
23 papers in training set
Top 0.1%
6.2%
6
PLOS Computational Biology
1633 papers in training set
Top 6%
6.2%
7
Nature Communications
4913 papers in training set
Top 33%
4.8%
8
Advanced Science
249 papers in training set
Top 4%
4.8%
50% of probability mass above
9
Communications Physics
12 papers in training set
Top 0.1%
4.2%
10
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 17%
4.1%
11
Nucleic Acids Research
1128 papers in training set
Top 5%
3.9%
12
iScience
1063 papers in training set
Top 11%
2.0%
13
eLife
5422 papers in training set
Top 36%
2.0%
14
Physical Review E
95 papers in training set
Top 0.5%
2.0%
15
Quantitative Biology
11 papers in training set
Top 0.3%
1.7%
16
Frontiers in Physics
20 papers in training set
Top 0.4%
1.7%
17
The Journal of Physical Chemistry Letters
58 papers in training set
Top 0.9%
1.7%
18
Science Advances
1098 papers in training set
Top 19%
1.6%
19
PNAS Nexus
147 papers in training set
Top 0.5%
1.3%
20
The Journal of Chemical Physics
49 papers in training set
Top 0.3%
1.3%
21
Computational and Structural Biotechnology Journal
216 papers in training set
Top 7%
1.1%
22
Journal of The Royal Society Interface
189 papers in training set
Top 4%
0.9%
23
Scientific Reports
3102 papers in training set
Top 72%
0.9%
24
Physical Biology
43 papers in training set
Top 2%
0.7%
25
Nature Physics
39 papers in training set
Top 1%
0.7%
26
Cell Reports
1338 papers in training set
Top 34%
0.7%
27
Communications Biology
886 papers in training set
Top 25%
0.7%
28
Biophysical Reports
36 papers in training set
Top 0.6%
0.6%