Chapter

Plant Developmental Biology - Biotechnological Perspectives

pp 3-20

Date:

Gene Regulatory Models for Plant Development and Evolution

  • E. R. Alvarez-BuyllaAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México Email author 
  • , M. BenítezAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México
  • , M. AldanaAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Campus Cuernavaca
  • , G. J. Escalera-SantosAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México
  • , Á. ChaosAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMDepartamento de Ecología Funcional, Instituto de Ecología, Universidad Nacional Autónoma de México
  • , P. Padilla-LongoriaAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMInstituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México
  • , R. Verduzco-VázquezAffiliated withC3, Centro de Ciencias de la Complejidad, Cd. Universitaria, UNAMUniversidad Autónoma del Estado de Morelos

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Abstract

We argue for the need of mathematical models as integrative tools for understanding processes of cell differentiation and morphogenesis, involving the concerted action of multiple components at different spatiotemporal scales during plant development. We propose dynamical models of gene regulatory networks (GRNs) as the basis for such means. Such models enable the identification of specific steady-state gene expression patterns (attractors), which correspond to different cell types. A comparison between discrete and continuous models is then presented, and we propose that the dynamical structure of a GRN subject to noise conceptually corresponds to Waddington's “epigenetic landscape”. In the third section, we review methods to infer GRN topology from microarray experiments. These include reverse engineering techniques such as Bayesian networks, mutual information, and continuous analysis models. We discuss the application of these approaches to plant cases. However, detailed molecular biology experiments have been very successful in deciphering the structure of underlying small networks. Therefore, we then focus our attention on GRN models of such small modules for various processes of plant development. The first example corresponds to a single-cell GRN for primordial cell specification during early stages of Arabidopsis thaliana flower development. Then, some examples of coupled GRN dynamics in spatiotemporal domains are recalled: cell differentiation in A. thaliana leaf and root epidermis, and the spatiotemporal pattern of genes responsible for the apical shoot meristem behavior. Furthermore, we consider models on auxin transport mechanisms that are sufficient to generate observed morphogenetic shoot and root patterns. We also present several approaches to model signal transduction pathways that consider crosstalk among several biochemical pathways, as well as the influence of environmental factors. In Section 1.5 we consider the constructive role of noise in pattern formation in complex systems. We finally conclude that studies on GRN structure and dynamics aid at understanding evolutionary morphological patterns.