Evidence map›Paper›PMID 29782484›Full record

ArticlePLoS computational biology2018

Implications for human odor sensing revealed from the statistics of odorant-receptor interactions.

Ji Hyun Bak, Seogjoo J Jang, Changbong Hyeon

Abstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Odor Thresholds in Flavor Science: A Data-Driven Review From Mechanisms to Applications.Comprehensive reviews in food science and food safety · 2026
    Review
  2. Article
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ji Hyun BakSchool of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.ORCID 0000-0002-5700-7823
Seogjoo J JangDepartment of Chemistry and Biochemistry, Queens College, City University of New York, Queens, New York, United States of America.
Changbong HyeonSchool of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.ORCID 0000-0002-4844-7237

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Binding of odorants to olfactory receptors (ORs) elicits downstream chemical and neural signals, which are further processed to odor perception in the brain. Recently, Mainland and colleagues have measured more than 500 pairs of odorant-OR interaction by a high-throughput screening assay method, opening a new avenue to understanding the principles of human odor coding. Here, using a recently developed minimal model for OR activation kinetics, we characterize the statistics of OR activation by odorants in terms of three empirical parameters: the half-maximum effective concentration EC50, the efficacy, and the basal activity. While the data size of odorants is still limited, the statistics offer meaningful information on the breadth and optimality of the tuning of human ORs to odorants, and allow us to relate the three parameters with the microscopic rate constants and binding affinities that define the OR activation kinetics. Despite the stochastic nature of the response expected at individual OR-odorant level, we assess that the confluence of signals in a neuron released from the multitude of ORs is effectively free of noise and deterministic with respect to changes in odorant concentration. Thus, setting a threshold to the fraction of activated OR copy number for neural spiking binarizes the electrophysiological signal of olfactory sensory neuron, thereby making an information theoretic approach a viable tool in studying the principles of odor perception.

Indexed as

Models, NeurologicalOdorantsOlfactory Receptor NeuronsReceptors, OdorantAnimalsComputational BiologyElectrophysiological PhenomenaHigh-Throughput Screening AssaysHumansKineticsReceptors, Odorant

Identifiers

PMID29782484
PMCPMC5983876

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.