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Modeling the Epigenetic Landscape in Plant Development

  • Jose Davila-Velderrain
  • Jose Luis Caldu-Primo
  • Juan Carlos Martinez-Garcia
  • Elena R. Alvarez-Buylla
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1819)

Abstract

Computational mechanistic models enable a systems-level understanding of plant development by integrating available molecular experimental data and simulating their collective dynamical behavior. Boolean gene regulatory network dynamical models have been extensively used as a qualitative modeling framework for such purpose. More recently, network modeling protocols have been extended to model the epigenetic landscape associated with gene regulatory networks. In addition to understanding the concerted action of interconnected genes, epigenetic landscape models aim to uncover the patterns of cell state transition events that emerge under diverse genetic and environmental background conditions. In this chapter we present simple protocols that naturally extend gene regulatory network modeling and demonstrate their use in modeling plant developmental processes under the epigenetic landscape framework. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature. The protocols presented here can be applied to any well-characterized gene regulatory network in plants, animals, or human disease.

Key words

Epigenetic landscape Gene regulatory networks Dynamical systems Systems biology Cell differentiation Attractors Morphogenesis Development 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jose Davila-Velderrain
    • 1
    • 2
    • 3
    • 4
  • Jose Luis Caldu-Primo
    • 1
  • Juan Carlos Martinez-Garcia
    • 2
  • Elena R. Alvarez-Buylla
    • 1
    • 5
    • 6
  1. 1.Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad UniversitariaMéxico D.FMexico
  2. 2.Departamento de Control Automático, Cinvestav-IPNMéxico D.FMexico
  3. 3.MIT Computer Science and Artificial Intelligence LaboratoryCambridgeUSA
  4. 4.Broad Institute of MIT and HarvardCambridgeUSA
  5. 5.Laboratorio de Genética Molecular, Desarrollo y Evolución de Plantas, Instituto de EcologíaMéxico D.FMexico
  6. 6.University of California, BerkeleyBerkleyUSA

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