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Related Experiment Video

Updated: Oct 8, 2025

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

Published on: September 25, 2019

Learning developmental mode dynamics from single-cell trajectories.

Nicolas Romeo^1,2, Alasdair Hastewell¹, Alexander Mietke¹

¹Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States.

|December 29, 2021

Summary

This summary is machine-generated.

Scientists developed a new computational framework to model embryonic development. This method uses cell migration data to understand symmetry breaking and create predictive models for developmental biology.

Keywords:

cell migration continuum model embryo physics of living systems spectral representation zebrafish

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Area of Science:

Developmental Biology
Biophysics
Computational Biology

Background:

Embryogenesis involves complex multicellular development driven by cell migration.
High-resolution microscopy reveals intricate cell dynamics during development.
Translating complex imaging data into predictive models remains a challenge.

Purpose of the Study:

To develop a computational framework for learning continuum models from live-cell imaging data.
To characterize developmental symmetry breaking in early zebrafish gastrulation.
To infer interpretable hydrodynamic models of collective cell migration.

Main Methods:

Utilized mode decomposition from physics and sparse dynamical systems inference.
Applied harmonic basis functions to coarse-grain and compress cell trajectory data on curved surfaces.
Mode-based model learning framework applied to zebrafish embryogenesis.

Main Results:

Successfully learned a low-dimensional representation of collective cell dynamics.
Enabled compact characterization of developmental symmetry breaking.
Inferred a hydrodynamic model showing similarities to active Brownian particle dynamics.

Conclusions:

Mode-based model learning provides a quantitative biophysical understanding of developmental processes.
The framework is applicable to various developmental structure formation processes.
Advances the study of cell migration in complex geometries during embryogenesis.