Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information

TL;DR

This paper introduces a model extending the French Flag concept, demonstrating how gene interactions and spatial cues encode positional information robustly in biological patterning.

Contribution

It presents a spin system model that explores optimal gene expression patterns, including Counter patterns, for encoding positional information in developmental biology.

Findings

Counter patterns encode positional information effectively.

Long-range interactions stabilize patterns against noise.

Model captures key features of biological patterning systems.

Abstract

A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert's paradigmatic "French Flag" model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call "Counter" patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework.