Back

Hydra morphogenesis as phase-transition dynamics

Agam, O.; Braun, E.

2023-04-06 biophysics
10.1101/2023.04.04.535567 bioRxiv
Show abstract

We utilize whole-body Hydra regeneration from a small tissue segment to develop a physics framework for animal morphogenesis. Introducing experimental controls over this process, an external electric field and a drug that blocks gap junctions, allows us to characterize the essential step in the morphological transition - from a spherical shape to an elongated spheroid. We find that spatial fluctuations of the Ca2+ distribution in the Hydras tissue drive this transition and construct a field-theoretic model that explains the morphological transition as a first-order-like phase transition resulting from the coupling of the Ca2+ field and the tissues local curvature. Various predictions of this model are verified experimentally.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Nature Physics
○ 45 papers in training set
Top 0.1%
28.0%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 10%
18.2%
Similar papers in this journal
3
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 4%
9.5%
Similar papers in this journal
50% of probability mass above
4
Biophysical Journal
○ 631 papers in training set
Top 2%
4.8%
Similar papers in this journal
5
eLife
● 5828 papers in training set
Top 29%
4.2%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 34%
3.2%
7
Communications Physics
○ 14 papers in training set
Top 0.1%
3.1%
8
Science Advances
● 1243 papers in training set
Top 14%
2.6%
9
Soft Matter
● 60 papers in training set
Top 0.4%
1.9%
10
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.9%
11
iScience
○ 1154 papers in training set
Top 18%
1.7%
12
PRX Life
● 42 papers in training set
Top 0.4%
1.7%
13
Physical Review Research
● 49 papers in training set
Top 0.5%
1.5%
14
PNAS Nexus
◐ 159 papers in training set
Top 1%
1.4%
15
The Journal of Chemical Physics
● 56 papers in training set
Top 0.4%
1.3%
16
Communications Biology
○ 993 papers in training set
Top 19%
1.3%
17
ACS Nano
● 113 papers in training set
Top 2%
1.1%
18
Physical Review Letters
● 47 papers in training set
Top 0.4%
1.1%
19
The Journal of Physical Chemistry Letters
● 63 papers in training set
Top 0.7%
1.0%
20
Nano Letters
● 71 papers in training set
Top 1%
1.0%
21
PLOS Computational Biology
● 1863 papers in training set
Top 20%
0.8%
22
The Journal of Physical Chemistry B
● 167 papers in training set
Top 2%
0.8%
23
Nature Materials
○ 28 papers in training set
Top 0.6%
0.8%
24
Physical Review X
● 25 papers in training set
Top 0.5%
0.8%

"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.