Predicting the response of olfactory sensory neurons to odor mixtures from single odor response.

Addolorata Marasco,Michele Migliore,Alessandro De Paris

doi:10.1038/srep24091

Abstract

The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

Highlights

The response of olfactory receptor neurons to odor mixtures is not well understood
We begin by considering the experimental findings on the activation of the olfactory sensory neurons (OSNs) as a function of an odor concentration, i.e. a dose-response curve for a specific OSN type
From a mathematical point of view, n modulates the steepness of the curve, KU the midpoint, and FMU the asymptotic value. This function has been used to successfully fit the great majority of OSN responses that have been experimentally reported so far for single odors. It needs to be generalized for mixtures, since the OSN response to a combination of odors cannot be obtained from the simple sum of the functions for the individual components

Summary

Results

We begin by considering the experimental findings on the activation of the olfactory sensory neurons (OSNs) as a function of an odor concentration, i.e. a dose-response curve for a specific OSN type. There can be another reason, related to the way in which the results for the mixture are determined and represented; we noted that in all cases a simple shift in the concentration assumed to plot the result for the mixture models (− 0.06, + 0.026, and − 0.1 log10(conc) for LIM + MEN, EVA + LYR, and CIT + LIL, respectively) would result in a much better agreement with experiments, especially in the lower concentration range (dotted line in left panels of Fig. 5). Our analysis predicts that it could be conveniently obtained with a mixture of odors activating the same OSNs (red and black symbols in Fig. 3D) combined with coefficients that can be mathematically calculated (see Appendix A.6)

Conclusions

Author Contributions

Additional Information