Abstract

The differentiation of the two cell types of the root epidermis, atrichoblasts, which give rise to hair cells, and atrichoblasts, which do not develop as hair cells, is determined by a complex regulatory network of transcriptional factors and hormones that act in concert in space and time to define a characteristic pattern of rows of hair cells and non-hair cells interspersed with each other throughout the root epidermis of Arabidopsis thaliana. Previous models have defined a minimal regulatory network that recovers the Wild Type phenotype and some mutants but fails to recover most of the mutant phenotypes, thus limiting its ability to spread. In this work, we propose a diffusion-coupled regulatory genetic network or meta-Gene Regulatory Network model extended to the model previously published by our research group, to describe the patterns of organization of the epidermis of the root epidermis of Arabidopsis thaliana. This network fully or partially recovers loss-of-function mutants of the identity regulators of the epidermal cell types considered within the model. Not only that, this new extended model is able to describe in quantitative terms the distribution of trichoblasts and atrichoblasts defined at each cellular position with respect to the cortex cells so that it is possible to compare the proportions of each cell type at those positions with that reported in each of the mutants analyzed. In addition, the proposed model allows us to explore the importance of the diffusion processes that are part of the lateral inhibition mechanism underlying the network dynamics and their relative importance in determining the pattern in the Wild Type phenotype and the different mutants.

Competing Interest Statement

The authors have declared no competing interest.

Details

Title
Extended discrete gene regulatory network model for the Arabidopsis thaliana root-hair cell fate
Author
Castillo-Jimenez, Aaron; Garay-Arroyo, Adriana; Maria De La Paz Sanchez-Jimenez; Garcia-Ponce, Berenice; Martinez-Garcia, Juan Carlos; Alvarez-Buylla, Elena R
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2023
Publication date
Nov 17, 2023
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2891035410
Copyright
© 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.