Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity

Qin, Shanshan; Li, Qianyi; Tang, Chao; Tu, Yuhai

doi:10.1073/pnas.1906571116

Cited by 19 publications

(31 citation statements)

References 92 publications

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“…Some authors have proposed that the random projections to cortex are a mechanism for creating sparse, high dimensional representations suitable for downstream linear classification [21, 39, 65], or are evidence for compressive sensing in olfaction [18, 25]. Others have suggested that compressive sensing occurs at the receptors [19, 27], and that the random projections reformat the compressed data for downstream decoding [27]. We propose a complementary view: random projections combine with strong gating to leverage information in silent receptors, enabling network decoding of responses from a small number of receptors.…”

Section: Resultsmentioning

confidence: 99%

“…There are perhaps 10 4 or more monomolecular odorants [1][2][3], far more than the number of receptor types in animals (⇠ 50 in fly, ⇠ 300 in human, ⇠ 1000 in rat, mouse and dog [4][5][6][7]). The problem of representing high-dimensional chemical space in a low-dimensional response space may be solved by the presence of many receptors that bind to numerous odorants [8][9][10][11][12][13][14], leading to a distributed, compressed, and combinatorial representation [12,[15][16][17][18][19][20][21] processed by activity in networks of neurons [22,23]. Some mechanisms for such distributed representation propose that each odorant activates specific subsets of neurons [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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What the odor is not: Estimation by elimination

Singh

Tchernookov

Balasubramanian

2019

Preprint

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The olfactory system uses the responses of a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. Here, we propose a method for decoding such a distributed representation. Our main idea is that a receptor that does not respond to an odor carries more information than a receptor that does, because a typical receptor binds to many odorants. So a response below threshold signals the absence of all such odorants. As a result, it is easier to identify what the odor is not, rather than what the odor is. We demonstrate that, for biologically realistic numbers of receptors, response functions, and odor mixture complexity, this remarkably simple method of elimination turns an underdetermined decoding problem into an overdetermined one, allowing accurate determination of the odorants in a mixture and their concentrations. We give a simple neural network realization of our algorithm which resembles the known circuit architecture of the piriform cortex.Olfaction | Receptors | Odor DecodingThe olfactory system enables animals to sense, perceive, and respond to mixtures of volatile molecules that carry messages about the world. There are many monomolecular odorants, perhaps 10 4 or more (1-3), far more than the number of receptor types available to animals (∼ 50 in fly, ∼ 300 in human, ∼ 1000 in rat, mouse and dog (4-7)). The problem of representing such a high-dimensional chemical space in such a low-dimensional response space may be solved by the presence of many receptors that bind to numerous odorants (8-14), leading to a distributed, combinatorial representation of odors (see, e.g., (12,(15)(16)(17)(18)(19)(20)(21)(22)(23)).We focus on the inverse problem: the estimation of odor composition from the response of olfactory receptors. We use a realistic competitive binding model of odor encoding by receptors (24)(25)(26)(27), and propose a scheme to decode odor composition from such responses. The scheme works over a large range of biologically relevant parameters, does not require any special constraints on receptor-odorant interactions, and works for systems with few receptors, suggesting why the relatively small olfactory receptor repertoires of most organisms are sufficient for detecting complex natural odors.Our main idea is that a receptor that does not respond to an odor carries a lot more information about the odor than a receptor that does respond to it. This is because a receptor that does not respond to an odor signals that none of the odorants (individual chemicals) that could bind to this receptor are present in the odor. With just a few such non-responding receptors, most of the odorants that are not present can be identified and eliminated. Thus, it is easier to identify what the mixture is not, rather than what the mixture is. For a large range of biologically relevant parameters, this elimination turns the estimation of odor concentration from an underdetermined problem to an overdetermined one. Thus, the concentration of the rest of the odorants can be estimated from...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What the odor is not: Estimation by elimination

Singh

Tchernookov

Balasubramanian

2019

Preprint

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“…Plotting the mutants versus normalized TAL and AHP shows 17 colonies with extreme activity and, generally, that mutants efficient at producing one product are inefficient at 18 producing the other (Figure 4A). To validate these results, we re-transform and test several mutants in bulk, 19 finding good agreement (Figures 4B, Figure S13, Table S3). Interestingly, the TLLL mutant based on 20 consensus design 18 of TAL enriched members from the UMAP also shows high TAL production, though it 21 retains higher AHP side-activity than the TLLN mutant 122 (Figure S14).…”

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confidence: 78%

Mapping enzyme catalysis with metabolomic biosensing

Chang

Payne

et al. 2021

Preprint

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Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis.

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“…Metabolic biosensing allows inference of product formation even without direct product detection because the enzyme’s activity perturbs the host cell’s metabolite profile. Provided such perturbations are distinct for each activity, all activities generated by a mutant library can be detected, similar to the “compressed sensing” of smell sensation 19 . Such indirect sensing has beneficial features for enzyme engineering.…”

Section: Discussionmentioning

confidence: 99%