Competitive and Noncompetitive Odorant Interactions in the Early Neural Coding of Odorant Mixtures

Abstract

Most olfactory receptor neurons (ORNs) express a single type of olfactory receptor that is differentially sensitive to a wide variety of odorant molecules. The diversity of possible odorant-receptor interactions raises challenging problems for the coding of complex mixtures of many odorants, which make up the vast majority of real world odors. Pure competition, the simplest kind of interaction, arises when two or more agonists can bind to the main receptor site, which triggers receptor activation, although only one can be bound at a time. Noncompetitive effects may result from various mechanisms, including agonist binding to another site, which modifies the receptor properties at the main binding site. Here, we investigated the electrophysiological responses of rat ORNs in vivo to odorant agonists and their binary mixtures and interpreted them in the framework of a quantitative model of competitive interaction between odorants. We found that this model accounts for all concentration-response curves obtained with single odorants and for about half of those obtained with binary mixtures. In the other half, the shifts of curves along the concentration axis and the changes of maximal responses with respect to model predictions, indicate that noncompetitive interactions occur and can modulate olfactory receptors. We conclude that, because of their high frequency, the noncompetitive interactions play a major role in the neural coding of natural odorant mixtures. This finding implies that the CNS activity caused by mixtures will not be easily analyzed into components, and that mixture responses will be difficult to generalize across concentration.

Modelling the signal delivered by a population of first-order neurons in a moth olfactory system

“…This other model was also about an ORN population in which all neurons express the same olfactory receptor but it was built differently and its numerical values come from another system. Each ORN was described by a biophysical model taking into account its biochemical, electrical and geometrical characteristics (Rospars et al, 1996(Rospars et al, , 2003(Rospars et al, , 2008. The series of conversions taking place during transduction (activated receptors, ionic conductances, receptor potential and firing frequency) were summarized as four main equations with 13 parameters.…”

A Biophysical Model of the Early Olfactory System of Honeybees

“…Unlike model in previous work, the model we developed here is not directly fitted to data, but to their overall statistics [3,4]. This allows us to study the statistical responses to different types of stimuli, with the trade-off that a generated model glomerulus may not correspond to any particular glomerulus in a honeybee.…”

“…Denoting * as the receptor response, the equilibrium response-dose relationship can be described by Hill curves when a time-invariant stimulus is applied [4]. , 1 ( −1 ) and 2 ( −2 ) are partially constrained by the parameters in the Hill curves, which are statistically sampled in accordance to experimental observations in [8].…”

“…However, it is unclear whether such models sufficiently relate to the biological systems they aim to describe, since the scaling of noise, synaptic efficacies and finite size network effects may change the dynamics of the system significantly. In addition, many numerical models create response patterns by directly fitting data from experiments in which specific properties of a subset of the systems are measured [3,4]. It cannot be expected that the predictions from these models would reflect other properties of the observed subsystem or any characteristics of the remainder of the system.…”

“…The resulting model comprises the full number of 160 different types of ORNs as well as local neurons (LNs) and projection neurons (PNs) organized in 160 corresponding glomeruli. The ORN response patterns are generated using a set of ordinary differential equations describing the binding and activation of receptors closely related to the actual biological processes [3,4], while the response of PNs are determined by network input from ORNs and LNs using a simple rate model derived from the leaky integrate-and-fire model.…”