Literature DB >> 28623593

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

José Dávila Velderraín1, Juan Carlos Martínez-García2, Elena R Álvarez-Buylla3,4.   

Abstract

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.

Keywords:  Attractor; Cell state dynamics; Cellular differentiation; Gene regulatory networks; Systems biology

Mesh:

Year:  2017        PMID: 28623593     DOI: 10.1007/978-1-4939-7125-1_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  Role of Cytokine Combinations on CD4+ T Cell Differentiation, Partial Polarization, and Plasticity: Continuous Network Modeling Approach.

Authors:  Mariana E Martinez-Sanchez; Leonor Huerta; Elena R Alvarez-Buylla; Carlos Villarreal Luján
Journal:  Front Physiol       Date:  2018-08-02       Impact factor: 4.566

2.  PlantSimLab - a modeling and simulation web tool for plant biologists.

Authors:  S Ha; E Dimitrova; S Hoops; D Altarawy; M Ansariola; D Deb; J Glazebrook; R Hillmer; H Shahin; F Katagiri; J McDowell; M Megraw; J Setubal; B M Tyler; R Laubenbacher
Journal:  BMC Bioinformatics       Date:  2019-10-21       Impact factor: 3.169

  2 in total

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