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Modeling the Epigenetic Landscape in Plant Development.

Jose Davila-Velderrain1,2,3,4, Jose Luis Caldu-Primo1, Juan Carlos Martinez-Garcia2

  • 1Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad Universitaria, México D.F, Mexico.

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Computational models enhance plant development understanding by simulating gene networks. New protocols extend these models to include epigenetic landscapes, revealing cell state transitions for broader applications.

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AttractorsCell differentiationDevelopmentDynamical systemsEpigenetic landscapeGene regulatory networksMorphogenesisSystems biology

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Area of Science:

  • Systems biology
  • Computational biology
  • Developmental biology

Background:

  • Boolean gene regulatory network (GRN) models offer qualitative insights into plant development.
  • Recent advancements extend GRN modeling to incorporate epigenetic landscape dynamics.
  • Epigenetic landscape models aim to predict cell state transitions under varying conditions.

Purpose of the Study:

  • To present simple protocols for extending GRN modeling to the epigenetic landscape framework.
  • To demonstrate the application of these protocols in modeling plant developmental processes.
  • To provide conceptual clarity and practical implementation guidance for researchers.

Main Methods:

  • Extending Boolean gene regulatory network dynamical models.
  • Integrating molecular experimental data into network models.
  • Simulating dynamical behavior and cell state transitions within the epigenetic landscape framework.

Main Results:

  • Developed protocols for modeling epigenetic landscapes alongside gene regulatory networks.
  • Demonstrated the utility of these protocols in simulating plant developmental processes.
  • Provided a framework applicable to diverse genetic and environmental conditions.

Conclusions:

  • The presented protocols offer a straightforward extension of GRN modeling to incorporate epigenetic dynamics.
  • These methods facilitate a deeper understanding of plant development and cell state transitions.
  • The protocols are broadly applicable to well-characterized GRNs in plants, animals, and human diseases.