Back

Reconstruction of the phase dynamics of the somitogenesis clock oscillator

Morales, L. J.; Dale, K. J.; Murray, P. J.

2019-08-22 developmental biology
10.1101/743724 bioRxiv
Show abstract

In this study we develop a computational framework for the reconstruction of the phase dynamics of the somitogenesis clock oscillator. Our understanding of the somitogenesis clock, a developmental oscillator found in the vertebrate embryo, has been revolutionised by the development of real time reporters of clock gene expression. However, the signals obtained from the real time reporters are typically noisy, nonstationary and spatiotemporally dynamic and there are open questions with regard to how post-processing can be used to both improve the insight gained from a given experiment and to constrain theoretical models. In this study we present a methodology, which is a variant of empirical mode decomposition, that reconstructs the phase dynamics of the somitogenesis clock. After validating the methodology using synthetic datasets, we define a set of metrics that use the reconstructed phase profiles to infer biologically meaningful quantities. We perform experiments in which the signal from a real time reporter of the somitogenesis clock is recorded and reconstruct the phase dynamics. Application of the defined metrics yields results that are consistent with previous experimental observations. Moreover, we extend previous work by developing a gradient descent method for defining automated kymographs and showing that boundary conditions are non-homogeneous. By studying phase dynamics along phase gradient descent trajectories, we show that, consistent with a previous theoretical model, the oscillation frequency is inversely correlated with the phase gradient but that the coefficient is not constant in time. The proposed methodology provides a tool kit for that can be used in the analysis of future experiments and the quantitative observations can be used to guide the development of future mathematical models.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
IFAC-PapersOnLine
13 papers in training set
Top 0.1%
15.1%
2
eLife
5828 papers in training set
Top 7%
11.9%
3
Journal of The Royal Society Interface
235 papers in training set
Top 0.3%
7.9%
4
Scientific Reports
3612 papers in training set
Top 11%
6.7%
5
Frontiers in Physiology
106 papers in training set
Top 0.2%
5.5%
6
PLOS Computational Biology
1863 papers in training set
Top 9%
4.0%
50% of probability mass above
7
Development
497 papers in training set
Top 2%
4.0%
8
PLOS ONE
5266 papers in training set
Top 38%
3.2%
9
Journal of Theoretical Biology
162 papers in training set
Top 1%
2.8%
10
Biology Open
156 papers in training set
Top 0.9%
2.6%
11
The Journal of Physiology
150 papers in training set
Top 0.9%
2.4%
12
npj Systems Biology and Applications
125 papers in training set
Top 0.8%
2.1%
13
Biophysical Journal
631 papers in training set
Top 3%
2.1%
14
Frontiers in Neuroscience
256 papers in training set
Top 3%
1.9%
15
eneuro
439 papers in training set
Top 5%
1.7%
16
Royal Society Open Science
214 papers in training set
Top 4%
1.5%
17
iScience
1154 papers in training set
Top 20%
1.5%
18
Frontiers in Neuroinformatics
41 papers in training set
Top 0.4%
1.4%
19
Journal of Cell Science
393 papers in training set
Top 3%
1.3%
20
Frontiers in Cell and Developmental Biology
233 papers in training set
Top 3%
1.3%
21
Bulletin of Mathematical Biology
92 papers in training set
Top 1%
0.8%
22
Bioinformatics
1204 papers in training set
Top 9%
0.8%
23
Journal of Anatomy
29 papers in training set
Top 0.5%
0.6%
24
MethodsX
16 papers in training set
Top 0.4%
0.6%
25
Mathematical Biosciences
49 papers in training set
Top 1%
0.6%