Cellular patterns in Arabidopsis root epidermis emerge from gene regulatory network and diffusion dynamical feedback

Area of Science:

• Systems Biology
• Developmental Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) control cell differentiation and spatial arrangement.
• Protein diffusion dynamics are crucial for pattern formation but often overlooked in models.
• Arabidopsis thaliana root epidermis exhibits a distinct pattern of hair and non-hair cells regulated by lateral inhibition.

Purpose of the Study:

• To develop and validate a systems biology model integrating GRNs with molecular diffusion for spatial pattern formation.
• To investigate the role of specific protein diffusion (CPC and GL3/EGL3) in Arabidopsis root epidermis patterning.
• To explore how feedback loops within GRNs and diffusion dynamics influence cell differentiation and spatial organization.

Main Methods:

• Developed a diffusion-coupled meta-GRN model incorporating positive and negative feedback loops.
• Utilized experimental data from wild-type and mutant Arabidopsis phenotypes for model validation.
• Simulated reaction-diffusion dynamics, including lateral inhibition and explicit diffusion of CPC and GL3/EGL3 proteins.

Main Results:

• The model accurately predicted 28 single and multiple loss-of-function mutant phenotypes.
• Successfully captured the spatial distributions of trichoblasts and atrichoblasts relative to cortex cells.
• Generated a 2-D morphospace illustrating epidermis patterning based on varying diffusion levels.

Conclusions:

• Protein diffusion plays a critical role in shaping cellular spatial configurations through dynamic feedback with GRNs.
• The proposed extended reaction-diffusion model provides new insights into biological pattern formation mechanisms.
• The findings underscore the importance of integrating diffusion dynamics into GRN models for accurate biological predictions.