Competitive binding predicts nonlinear responses of olfactory receptors to complex mixtures

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 an...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 116; no. 19; pp. 9598 - 9603
Main Authors Singh, Vijay, Murphy, Nicolle R., Balasubramanian, Vijay, Mainland, Joel D.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 07.05.2019
Subjects
Aroma compounds
Binding sites
Biological Sciences
Cell Line
Color
Color vision
Humans
Light effects
Models, Biological
Nonlinear systems
Nonlinearity
Odorant receptors
Odorants
Odors
Olfaction
Physical Sciences
Receptors
Receptors, Odorant - genetics
Receptors, Odorant - metabolism
Wavelengths
olfaction
sensory coding
odor mixtures
olfactory receptor neurons
receptor biophysics
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Summary: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 12 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.
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Edited by Katherine Nagel, New York University, New York, NY, and accepted by Editorial Board Member John R. Carlson March 27, 2019 (received for review August 2, 2018)
2V.B. and J.D.M. contributed equally to this work.
Author contributions: V.S., V.B., and J.D.M. designed research; V.S., N.R.M., V.B., and J.D.M. performed research; V.S., V.B., and J.D.M. analyzed data; and V.S., V.B., and J.D.M. wrote the paper.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1813230116