2024/10/04 by Albert Alonso, Robert G. Endres, Julius B. Kirkegaard · 1 voice
Physics and Astronomy · Computer Science · Biochemistry, Genetics and Molecular Biology · #physics.bio-ph #cond-mat.soft #cs.IT #q-bio.CB
paper · pdf · doi:10.1103/physrevlett.134.158401
arxiv published 2024/10/04 · arxiv updated 2025/04/16
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.