Membrane clustering and the role of rebinding in biochemical signaling

TL;DR

This paper investigates how membrane clustering influences biochemical signaling, revealing that clustering can both suppress and enhance responses depending on the network's modification level, through simulation and analytic models.

Contribution

It demonstrates the dual role of membrane clustering in biochemical signaling, highlighting local rebinding effects and providing combined simulation and analytic insights.

Findings

Clustering reduces single-modification network response.

Clustering enhances double-modification network response.

Optimal rebinding occurs at specific diffusion coefficients.

Abstract

In many cellular signaling pathways, key components form clusters at the cell membrane. Although much work has focused on the mechanisms behind such cluster formation, the implications for downstream signaling remain poorly understood. Here, motivated by recent experiments, we study via particle-based simulation a covalent modification network in which the activating component is either clustered or randomly distributed on the membrane. We find that while clustering reduces the response of a single-modification network, clustering can enhance the response of a double-modification network. The reduction is a bulk effect: a cluster presents a smaller effective target to a substrate molecule in the bulk. The enhancement, on the other hand, is a local effect: a cluster promotes the rapid rebinding and second activation of singly active substrate molecules. As such, the enhancement relies upon frequent collisions on a short timescale, which leads to a diffusion coefficient at which the enhancement is optimal. We complement simulation with analytic results at both the mean-field and first-passage distribution levels. Our results emphasize the importance of spatially resolved models, showing that significant effects of spatial correlations persist even in spatially averaged quantities such as response curves.