Theoretical upper bound of multiplexing in stochastic sensory receptors

TL;DR

This paper develops a theoretical framework demonstrating that stochastic biological sensors can multiplex multiple environmental signals simultaneously, challenging the view that noise is purely detrimental and providing bounds on sensory capacity.

Contribution

It introduces a general theory of stochastic sensory multiplexing, establishes upper bounds on sensor performance, and proposes a systematic method for assessing sensory capabilities.

Findings

Binary-state receptors can encode multiple environmental variables simultaneously.

Random sensors tend to saturate the tighter theoretical upper bound.

The rd-MLE framework enables comprehensive assessment of sensor performance.

Abstract

Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.