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Turing-like mechanism in a stochastic reaction-diffusion model recreates three dimensional vascular patterning of plant stems

Haslett MB, Bhowmik A, Bhide K, Bhide K

plant-growthmorphogenesisturing-patternsreaction-diffusionvascular-patterningcomputational-modeling

Abstract

A stochastic reaction-diffusion model of plant vascular bundle patterning was developed to highlight a potential mechanism of three dimensional patterning through Turing pattern formation coupled with longitudinal efflux of a regulatory molecule. The HBPM model recreated three dimensional plant stem vascular patterning using a Turing-like mechanism in a stochastic, reaction-diffusion framework.

Summary

This paper provides computational proof that Turing instability mechanisms can generate the precise three-dimensional vascular patterns observed in plant stems. It directly validates the “tissues in gradients” concept by showing how cells at different positions in a chemical gradient field differentiate into distinct vascular structures.

Key computational insights:

  1. Stochastic Turing mechanism: The model uses a Turing-like instability in a stochastic framework, demonstrating that reaction-diffusion dynamics with random fluctuations can robustly generate vascular patterns.

  2. Three-dimensional patterning: Unlike many 2D models, this work extends to 3D, showing how longitudinal efflux of regulatory molecules creates coherent patterns along the stem axis.

  3. Diffusion rate sensitivity: Altered diffusion rates of activator and substrate molecules affected the number and size of simulated vascular bundles - demonstrating quantitative control through gradient parameters.

  4. Tissue width threshold: Vascular bundles failed to develop below a threshold width of parenchymatous tissue, suggesting that spatial context (gradient field size) determines developmental outcomes.

  5. Evolutionary implications: The model explains how changes in tissue geometry can lead to evolutionary loss of vascular features - gradient fields that are too small cannot support pattern formation.

This work provides the computational rigor needed to understand how gradient fields instruct tissue differentiation in plant development.

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