Investigating the regulation of gene patterning in plant meristems and roots with gene regulatory network models

Dnr:

SNIC 2017/1-122

Type:

SNAC Medium

Principal Investigator:

Pawel Krupinski

Affiliation:

Lunds universitet

Start Date:

2017-03-15

End Date:

2017-10-01

Primary Classification:

10614: Utvecklingsbiologi

Webpage:

http://home.thep.lu.se/~henrik/

Allocation

Abstract

In the shoot apical meristem, a population of stem cells are maintained throughout the life of the plant. The meristem is divided into the central zone, which includes the population of stem cells, and the peripheral zone, in which cells differentiates into more specialized cell types. Our aim is to understand how these zones are created and maintained, by gene regulatory networks acting on a cellular level, producing a very organized regulation of a coordinated differentiation and morphogenesis. To address this we want to investigate possible gene regulatory networks for creating stable spatial patterns. We develop and use simulation tools for this problem with a main focus on solving ordinary differential equation models. In this project, we aim to test how the patterning capabilities of our models depends on which interactions are included. To do this we need to run many sets of optimizations in order to find good parameter sets for each model we want to compare. Similarily, in the root, meristematic cells are present in a region called the root apical meristem. These cells grow and divide, and move away from the root apex to become differentiated. Cells in the epidermal layers of the root can develop root hairs, as they move from the root apex and become differentiated, possibly depending on an tissue/cell gradient of the plant hormone auxin . Sites of root hair formation within a cell are marked by sites with high concentrations of Rho-of-plant proteins (ROPs). The distribution of ROPs and the auxin gradient in the tissue is governed by reaction-diffusion interactions. We investigate models based on such interactions by optimizing the auxin gradient to the correct large-scale tissue gradient from data, and compare the single cell model behaviour to that of the phenotypes from wild type and mutant data. The application for a medium project will allow us to compare different models in enough detail with the possibility to run many parallell simulations/optimizations at once in the HPC structures.