Morphogenesis by coupled regulatory networks: Reliable control of positional information and proportion regulation

TL;DR

This paper explores how coupled gene-like regulatory networks can reliably control spatial patterning and proportion regulation in developing tissues, demonstrating robustness to noise and cell movement.

Contribution

It introduces a hierarchical network model for pattern formation that maintains precise boundaries and proportion regulation despite noise and dynamic cell flow.

Findings

Networks exhibit hierarchical information processing similar to biological systems.

Proportion regulation scales with system size and maintains sharp boundaries.

Noise stabilizes spatial patterns in dynamic, growing tissues.

Abstract

Based on a non-equilibrium mechanism for spatial pattern formation we study how position information can be controlled by locally coupled discrete dynamical networks, similar to gene regulation networks of cells in a developing multicellular organism. As an example we study the developmental problems of domain formation and proportion regulation in the presence of noise, as well as in the presence of cell flow. We find that networks that solve this task exhibit a hierarchical structure of information processing and are of similar complexity as developmental circuits of living cells. Proportion regulation is scalable with system size and leads to sharp, precisely localized boundaries of gene expression domains, even for large numbers of cells. A detailed analysis of noise-induced dynamics, using a mean-field approximation, shows that noise in gene expression states stabilizes (rather than disrupts) the spatial pattern in the presence of cell movements, both for stationary as well as growing systems. Finally, we discuss how this mechanism could be realized in the highly dynamic environment of growing tissues in multi-cellular organisms.