A competitive binding model predicts nonlinear responses of olfactory receptors to complex mixtures

TL;DR

This paper introduces a biophysical competitive binding model that accurately predicts mammalian olfactory receptor responses to complex odor mixtures, capturing nonlinear effects like synergy and inhibition with limited data.

Contribution

The study presents a novel competitive binding model for olfactory receptors that effectively predicts responses to complex mixtures, advancing understanding of olfactory coding.

Findings

Predicts receptor responses within 15% of experimental data for mixtures of up to 12 odorants.

Models phenomena such as synergy, overshadowing, and inhibition.

Identifies interactions through deviations from the competitive binding model.

Abstract

In color vision, the quantitative rules for mixing lights to make a target color are well understood. By contrast, the rules for mixing odorants to make a target odor remain elusive. A solution to this problem in vision relied on characterizing receptor responses to different wavelengths of light and subsequently relating these responses to perception. In olfaction, experimentally measuring receptor responses to a representative set of complex mixtures is intractable due to the vast number of possibilities. To meet this challenge, we develop a biophysical model that predicts mammalian receptor responses to complex mixtures using responses to single odorants. The dominant nonlinearity in our model is competitive binding (CB): only one odorant molecule can attach to a receptor binding site at a time. This simple framework predicts receptor responses to mixtures of up to twelve monomolecular odorants to within 15\% of experimental observations and provides a powerful method for leveraging limited experimental data. Simple extensions of our model describe phenomena such as synergy, overshadowing, and inhibition. We demonstrate that the presence of such interactions can be identified via systematic deviations from the competitive binding model.