Generative epigenetic landscapes map the topology and topography of cell fates
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new generative model for building Waddington landscapes from data, enabling systematic inference of cell fate dynamics. The approach reveals interpretable and predictive epigenetic landscapes, advancing developmental biology research.
Area Of Science
- Developmental Biology
- Systems Biology
- Computational Biology
Background
- Waddington's epigenetic landscapes conceptualize cell fate dynamics.
- Modern models use attractors and stochasticity for cell decisions.
- Systematic tools to infer and build these landscapes are lacking.
Purpose Of The Study
- To propose a generative model for deriving epigenetic landscapes compatible with biological data.
- To develop interpretable models by combining gradient and rotational vector fields.
- To enable systematic inference and construction of Waddington landscapes.
Main Methods
- Developed a generative model combining gradient and rotational vector fields.
- Utilized locally weighted elements to encode landscape valleys.
- Employed computational evolution for landscape optimization.
- Applied the model to metazoan segmentation and neuromesoderm differentiation.
Main Results
- Successfully derived ensembles of epigenetic landscapes for developmental examples.
- Revealed both known and novel landscape topologies and bifurcations.
- Demonstrated that topographic features are constrained by dynamical data.
- Generated interpretable and predictive epigenetic landscape models.
Conclusions
- The proposed generative model provides a systematic approach to infer epigenetic landscapes.
- The method yields interpretable models constrained by dynamical data.
- This approach facilitates quantitative and predictive descriptions of cell fate dynamics.
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