Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

TL;DR

This study uses simulations to explore how the physical space occupied by molecules on biomembranes affects biochemical signaling, revealing complex behaviors and molecular distributions influenced by excluded volume effects.

Contribution

It introduces a 2D cell-based model to analyze how excluded volume impacts signaling dynamics and molecular organization on biomembranes, highlighting non-trivial variations in signal flow.

Findings

Excluded volume causes non-monotonic signal flow variations.

Molecular distributions form receptor-signaling clusters at high signal flow.

Signal flow exhibits bell-shaped and S-shaped dependencies on target protein binding rates.

Abstract

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.