Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction

  • José Dávila Velderraín
  • Juan Carlos Martínez-García
  • Elena R. Álvarez-Buylla
Part of the Methods in Molecular Biology book series (MIMB, volume 1629)


Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

Key words

Systems biology Gene regulatory networks Cellular differentiation Cell state dynamics Attractor 


  1. 1.
    Alvarez-Buylla ER, Benítez M, Davila EB, Chaos A, Espinosa-Soto C, Padilla-Longoria P (2007) Gene regulatory network models for plant development. Curr Opin Plant Biol 10(1):83–91CrossRefPubMedGoogle Scholar
  2. 2.
    Huang S, Kauffman S (2009) Complex gene regulatory networks—from structure to biological observables: cell fate determination. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, Heidelberg, pp 1180–1213CrossRefGoogle Scholar
  3. 3.
    Albert I, Thakar J, Li S, Zhang R, Albert R (2008) Boolean network simulations for life scientists. Source Code Biol Med 3:16CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Azpeitia E, Davila-Velderrain J, Villarreal C et al (2014) Gene regulatory network models for floral organ determination. In: Riechmann JL, Wellmer F (eds) Flower development. Springer, New York, NY, pp 441–469CrossRefGoogle Scholar
  5. 5.
    Davila-Velderrain J, Martinez-Garcia JC, Alvarez-Buylla ER (2015) Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development. Front Genet 6:160CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kauffman S (1969) Homeostasis and differentiation in random genetic control networks. Nature 224:177–178CrossRefPubMedGoogle Scholar
  7. 7.
    Mendoza L, Alvarez-Buylla ER (1998) Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. J Theor Biol 193(2):307–319CrossRefPubMedGoogle Scholar
  8. 8.
    Azpeitia E, Benítez M, Vega I, Villarreal C, Alvarez-Buylla ER (2010) Single-cell and coupled GRN models of cell patterning in the Arabidopsis thaliana root stem cell niche. BMC Syst Biol 4:134CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Benítez M, Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER (2008) Interlinked nonlinear subnetworks underlie the formation of robust cellular patterns in Arabidopsis epidermis: a dynamic spatial model. BMC Syst Biol 2(1):98CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pérez-Ruiz RV, García-Ponce B, Marsch-Martínez N et al (2015) XAANTAL2 (AGL14) is an important component of the complex gene regulatory network that underlies arabidopsis shoot apical meristem transitions. Mol Plant 8(5):796–813CrossRefPubMedGoogle Scholar
  11. 11.
    Müssel C, Hopfensitz M, Kestler HA (2010) BoolNet—an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics 26(10):1378–1380CrossRefPubMedGoogle Scholar
  12. 12.
    Kaplan D, Glass L (2012) Understanding nonlinear dynamics. Springer, New York, NYGoogle Scholar
  13. 13.
    Ellner SP, Guckenheimer J (2011) Dynamic models in biology. Princeton University Press, Princeton, NYGoogle Scholar
  14. 14.
    Garg A, Mohanram K, De Micheli G, Xenarios I (2012) Implicit methods for qualitative modeling of gene regulatory networks In Gene Regulatory Networks. Humana, New York, NY, pp 397–443Google Scholar
  15. 15.
    Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16(11):2923–2939CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Gershenfeld N (1998) The nature of mathematical modeling. Cambridge University Press, CambridgeGoogle Scholar
  17. 17.
    Arellano G, Argil J, Azpeitia E et al (2011) “Antelope”: a hybrid-logic model checker for branching-time Boolean GRN analysis. BMC Bioinformatics 12:490CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Naldi A, Berenguier D, Fauré A et al (2009) Logical modeling of regulatory networks with ginsim 2.3. Biosystems 97(2):134–139CrossRefPubMedGoogle Scholar
  19. 19.
    Corblin F, Fanchon E, Trilling L (2010) Applications of a formal approach to decipher discrete genetic networks. BMC Bioinformatics 11(1):385CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    De Jong H, Geiselmann J, Hernandez C et al (2003) Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19(3):336–344CrossRefPubMedGoogle Scholar
  21. 21.
    Calzone L, Fages F, Soliman S (2006) Biocham: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14):1805–1807CrossRefPubMedGoogle Scholar
  22. 22.
    Yuan R, Zhu X, Radich JP, Ao P (2016) From molecular interaction to acute promyelocytic leukemia: calculating leukemogenesis and remission from endogenous molecular-cellular network. Sci Rep 6:24307CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Azpeitia E, Weinstein N, Benítez M, Mendoza L, Alvarez-Buylla ER (2013) Finding missing interactions of the Arabidopsis thaliana root stem cell niche gene regulatory network. Front Plant Sci 4:110CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • José Dávila Velderraín
    • 1
  • Juan Carlos Martínez-García
    • 2
  • Elena R. Álvarez-Buylla
    • 1
    • 3
  1. 1.Centro de Ciencias de la Complejidad (C3)Universidad Nacional Autónoma de México (UNAM)MéxicoMexico
  2. 2.Departamento de Control AutomáticoCinvestav-IPNMéxicoMexico
  3. 3.Laboratorio de Genética Molecular, Desarrollo y Evolución de Plantas, Instituto de EcologíaUniversidad Nacional Autónoma de México (UNAM)Mexico CityMexico

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