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Updated: Sep 22, 2025

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
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Computational Systems Biology of Morphogenesis.

Jason M Ko1, Reza Mousavi1, Daniel Lobo2

  • 1Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 23, 2022
PubMed
Summary

Understanding complex developmental patterns in morphogenesis requires a computational systems biology approach. This method integrates modeling, machine learning, and predictions to uncover the mechanistic basis of biological development.

Keywords:
Computational BiologyMachine LearningMorphogenesisSystems Biology

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Area of Science:

  • Developmental biology
  • Systems biology
  • Computational biology

Background:

  • Morphogenesis involves complex biological regulation, feedback loops, and biophysical forces.
  • Understanding the mechanistic basis of spatial and temporal phenotypes in development is challenging.

Purpose of the Study:

  • To present a computational systems biology approach for understanding morphogenesis.
  • To provide tools and guidelines for implementing this methodology.

Main Methods:

  • Integrating tissue and whole-embryo modeling with dynamical systems.
  • Employing machine learning for reverse engineering of parameters and equations.
  • Generating testable computational predictions for experimental validation.

Main Results:

  • Demonstrated a computational methodology to aid in understanding morphogenesis.
  • Developed user-friendly tools, computer code, and guidelines for implementation.
  • Established a generalizable framework adaptable to various model organisms.

Conclusions:

  • Computational systems biology offers a powerful approach to deciphering developmental mechanisms.
  • This methodology facilitates the extraction of mechanistic knowledge from complex biological systems.
  • The presented framework can advance the study of morphogenesis across different species.