Thursday, June 3, 2021

Artificial Olfactory Perception and the Olfactome

Chemical informatics, machine learning and the indispensable fruit-fly, Drosophila melanogaster have been used by researchers at University of California Riverside to predict odor perception. 

Olfactory prediction is kind of a holy grail of sensory perception. Sounds sus. Let's get into the data.

image credit: Diatom, by Dr. Jan Michels for Nikon Small World 2020

Using artificial intelligence to smell the roses
Aug 2020,

First sentence they're referencing Asifa Majid. That's a great start. Her work shows us that culture, language and experience influence individual odor perception. Nonetheless, the search for the human odor code continues.

After reducing a larger dataset of 84 olfactory receptors and 54 allelic variants (138 total), they took 34 receptors, each of which is controlled by a single gene, and trained machines to predict their descriptors. The descriptors, or "the words we would use to describe the smell," came from the Vosshall Keller Rockefeller University 2016 lexicon. They've got about 170 odorants, working on 34 receptors. 

Remember that each odor receptor gene can be activated by a number of chemicals, sometimes by only one, but usually by more than one. This is what makes things complicated. Olfaction is a combinatorial affair that breaks down at the granular level.

And they made a model for each receptor, 34 different models, and fed those models the odorants. They found that you could predict chemical properties of the molecules that match each receptor tested. So now, we can use the 450,000 library of chemicals, run them through each of the 36 artificial receptors, and predict what those receptors would perceive.

Figure 5A: Few Key ORs or Chemical Features Sensibly Cluster the Perceptual Descriptors
(A) Dendrogram representation of the Euclidean distances among perceptual descriptors based on overlap of perceptual response data (% Usage) from chemicals in the ATLAS study.
(B) Dendrogram from the top five ORs picked per perceptual descriptor.
(C) Dendrogram created from five randomly chosen ORs per perceptual descriptor.
(D) Dendrogram from the five best overall predictors including OR and chemical features per perceptual descriptor. Clustering is hierarchical and based on Euclidean distance (A) or the Jaccard distance (B–D). Cluster number (colored branches) inferred from gap statistic across bootstrap samples. [find the pdf for fine-resolution]

I think, and I could be wrong, but it seems the big deal here is that they made a model for each receptor, instead of just making one model for all receptors. Whereas others have created an n-dimensional predictive space to collapse the behemoth of the chemosphere into a single equation, this team just reverse-engineered the receptors themselves.

They haven't found the odor code, but they did write 34 of them. We have hundreds of olfactory receptors. That's not everything, but we are definitely getting there.

What it CAN do? It can help us discover new chemicals, and also to discover substitutes for other chemicals that are expensive, rare, or ethically-troublesome (fear-pheromones from tortured cats for example).

What it CAN'T do? It can't predict how an odor will smell to you, as an individual. It can approximate, however, and pretty good. They mention only getting 20% of the human olfactome, or human olfactory receptor repertoire.

via UC Riverside: Joel Kowalewski et al. Predicting Human Olfactory Perception from Activities of Odorant Receptors, iScience (2020). DOI: 10.1016/j.isci.2020.101361

Post Script:
They mention something called the ATLAS dataset, but I don't know what that is, other than a proprietary data analysis software. Maybe it's their own dataset through ATLAS?

And for fun, I'll report that they do mention "substantive portion of odor identity arises early in the processing stream" which is a good way of describing the the two-layer perception process of olfaction.

The second layer, and this is the one that Asifa Majid tells us is influenced by culture, experience, and language: "It is likely that the remaining portion depends on experience-dependent modulation, supporting a downstream model with reliance on distributed neuronal networks for human perceptual coding."

Further: "Unlike the retinotopic and tonotopic patterning observed in the visual and auditory cortices, representing spatiotemporal properties of visual and auditory stimuli as they are processed at sensory neurons, piriform activity appears randomly distributed, without a clear mapping of physicochemical features (Stettler and Axel, 2009)."

Interesting: "In our analyses, the OR specialized for musk was not a top candidate for
musk predictions but contributed strongly to predictions of 'sweaty.'"

Perhaps because the model isn't "smelling" it among other calculated fragrant mixtures such as perfumes, but rather "in the wild?" 

Post Post Script:
Can't talk about the odor code without mentioning code smell, a term for when something is wrong with your code, but we're not sure what it is. 

Also, going deep on the topic here:
The Dream of Olfaction Prediction

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