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Structural causes of pattern formation and loss through model-independent bifurcation analysis
Liam D O'Brien1, Adriana T Dawes2,3
1Department of Mathematics, The Ohio State University, 231 W 18th Ave, Columbus, 43210, Ohio, USA. obrien.1093@osu.edu.
Journal of Mathematical Biology
|October 27, 2025
Summary
This study presents a new framework for understanding how cells form patterns during development. It shows that intracellular signaling constraints lead to a single, stable cell fate pattern, aiding in network inference.
Area of Science:
- Developmental Biology
- Systems Biology
- Biophysics
Background:
- Precise cellular patterning is crucial for tissue and organ formation during development.
- Conserved signaling networks regulate intracellular and intercellular communication, driving pattern emergence.
- Existing models often rely on specific equations, limiting generalizability.
Purpose of the Study:
- To develop a model-independent framework for analyzing pattern formation in homogeneous cell arrays.
- To investigate how intracellular signaling constraints influence emergent cell fate patterns.
- To provide a tool for inferring signaling network interactions from tissue-level patterns.
Main Methods:
- Utilized an ordinary differential equation (ODE) framework.
- Focused on general assumptions of global intercellular communication and qualitative intracellular signaling properties.
- Built upon prior work demonstrating the role of intercellular communication networks in pattern determination.
Main Results:
- Demonstrated that constraints on local intracellular signaling networks lead to a single stable pattern.
- Showed this stable pattern is dependent on the qualitative features of the intracellular network.
- The framework enables pattern prediction with minimal modeling assumptions.
Conclusions:
- The developed framework offers a powerful, model-independent approach to studying cell fate patterning.
- It facilitates the inference of unknown signaling network interactions by analyzing emergent tissue patterns.
- This work advances the understanding of developmental processes and signaling network dynamics.
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