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Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System

Overview of attention for article published in PLoS Computational Biology, May 2013
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Title
Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003026
Pubmed ID
Authors

Rafael A. Barrio, José Roberto Romero-Arias, Marco A. Noguez, Eugenio Azpeitia, Elizabeth Ortiz-Gutiérrez, Valeria Hernández-Hernández, Yuriria Cortes-Poza, Elena R. Álvarez-Buylla

Abstract

A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate, yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ. In this paper we propose that, during development, cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating. This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system. We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular, genetic and cellular levels and has the key traits of multicellular stem-cell niches. We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics, both of which are known to play a role in root development. We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip. Our model recovers, as an emergent pattern, the transition from proliferative to transition and elongation domains, characteristic of stem-cell niches in multicellular organisms. In addition, we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions. Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems.

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Geographical breakdown

Country Count As %
Mexico 5 6%
United Kingdom 1 1%
France 1 1%
Unknown 76 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 30%
Student > Bachelor 12 14%
Student > Master 10 12%
Student > Ph. D. Student 9 11%
Professor 6 7%
Other 16 19%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 57%
Physics and Astronomy 8 10%
Biochemistry, Genetics and Molecular Biology 8 10%
Computer Science 2 2%
Mathematics 2 2%
Other 8 10%
Unknown 8 10%