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Quantifying cell transitions in C. elegans with data-fitted landscape models.

Elena Camacho-Aguilar1,2, Aryeh Warmflash2,3, David A Rand1,4

  • 1Mathematics Institute, University of Warwick, Coventry, United Kingdom.

Plos Computational Biology
|June 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework using Catastrophe Theory and approximate Bayesian computation to model complex cell differentiation, specifically vulval development in C. elegans. The developed model reveals a quantifiable two-step decision process driven by key signaling pathways.

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Area of Science:

  • Developmental biology
  • Mathematical modeling
  • Cell differentiation

Background:

  • Waddington's landscape metaphor is increasingly used to study complex differentiation.
  • A rational method for building these landscape models is currently lacking.
  • Vulval development in C. elegans serves as a model system for cell fate decisions.

Purpose of the Study:

  • To develop a rational framework for building data-fitted landscape models.
  • To apply this framework to understand vulval development in C. elegans.
  • To identify the underlying signaling mechanisms driving cell fate decisions.

Main Methods:

  • Utilized Catastrophe Theory (CT) to identify candidate qualitative landscapes.
  • Employed approximate Bayesian computation (ABC) for quantitative model fitting.
  • Integrated experimental data with theoretical modeling for vulval development.

Main Results:

  • Developed a novel framework combining CT and ABC for landscape model construction.
  • The model elucidates a quantifiable two-step decision mechanism in vulval development.
  • Identified EGF and Notch-Delta signaling as key controllers of the cell fate decision.

Conclusions:

  • The new model provides a robust fit to existing data on C. elegans vulval development.
  • The framework offers a rational approach to building predictive landscape models.
  • The study makes several novel predictions regarding developmental signaling pathways.