Local Clustering and Global Spreading of Receptors for Optimal Spatial Gradient Sensing

TL;DR

This paper presents a theoretical model explaining how cell-surface receptors optimally cluster in high-curvature regions to enhance gradient sensing accuracy, aligning with observed biological patterns.

Contribution

The study introduces a model that predicts receptor clustering based on minimizing sensing uncertainty, without relying on prior physical or biochemical limits.

Findings

Receptors tend to cluster in high-curvature regions to reduce sensing uncertainty.

On spherical surfaces, receptors are evenly distributed unless symmetry is broken.

The model applies to motile receptors responding to shape changes and fluid flow.

Abstract

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the relevance of our model in realistic scenarios. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.