Robust boundary formation in a morphogen gradient via cell-cell signaling

TL;DR

This paper compares three cell-cell signaling mechanisms (AND, OR, SUM) combined with a morphogen gradient to improve boundary precision in gene expression patterns, showing that all outperform gradient-only methods especially under high noise.

Contribution

It introduces a conceptual model analyzing how different local signaling rules enhance boundary formation accuracy in noisy environments.

Findings

All three mechanisms reduce boundary fuzziness compared to gradient-only.

The SUM rule yields the most precise boundary but needs noise to escape metastability.

Mechanisms improve boundary tunability and scale with system size.

Abstract

Establishing sharp and correctly positioned boundaries in spatial gene expression patterns is a central task, both in developmental and synthetic biology. We consider situations where a global morphogen gradient provides positional information to cells, but is insufficient to ensure the required boundary precision, due to different types of noise in the system. In a conceptual model, we quantitatively compare three mechanisms, which combine the global signal with local signaling between neighboring cells, to enhance the boundary formation process. These mechanisms differ with respect to the way in which they combine the signals, by following either an AND, an OR, or a SUM rule. Within our model, we analyze the dynamics of the boundary formation process, and the fuzziness of the resulting boundary. Furthermore, we consider the tunability of the boundary position, and its scaling with system size. We nd that all three mechanisms produce less fuzzy boundaries than the purely gradient-based reference mechanism, even in the regime of high noise in the local signals relative to the noise in the global signal. Among the three mechanisms, the SUM rule produces the most accurate boundary. However, in contrast to the other two mechanisms, it requires noise to exit metastable states and rapidly reach the stable boundary pattern.