Biology subjects

Gerkin, R. C.

Publications and source records attributed to Gerkin, R. C..

16 recordsLinked to original sources

bioRxiv2026

Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception

Most smells, whether in food, perfume, or the environment, are complex mixtures of many odor molecules. A principled framework for modeling natural mixtures has been considered intractable because mixtures are widely assumed to generate nonlinear perceptual interactions that mirror nonlinear responses at the receptor and neural levels. We quantified perceptual interactions across 432 mixtures composed of 144 component odorants using a trained human panel, and nearly all mixtures were explained by a linear average of their component quality profiles. Within this linear modeling framework, averaging component profiles predicted mixture perception significantly better than the previous state-of-the-art and approached the ceiling set by measurement noise. Even mixtures previously reported to exhibit emergence were equally well predicted. These results suggest that the main challenge in modeling odor perception is characterizing individual components, rather than exhaustively mapping odor-odor interactions. Just as additive color mixing enabled colorimetry, linear mixing in olfaction opens the door to quantitative odorimetry, allowing mixtures to be predicted, reconstructed, and optimized computationally.

neuroscience↗

bioRxiv2025

A quantitative framework for predicting odor intensity across molecule and mixtures

In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors.

neuroscience↗

bioRxiv2022

Pyrfume: A Window to the World's Olfactory Data

Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Olfactory neuroscience and psychophysics remain years behind the other senses in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways underlying this transformation, remain scarce. Here we present a concerted effort to unlock and unify dozens of stimulus-linked olfactory datasets across species and modalities under a unified framework called Pyrfume. We present examples of how researchers might use Pyrfume to conduct novel analyses uncovering new principles, introduce trainees to the field, or construct benchmarks for machine olfaction.

neuroscience↗

bioRxiv2022

A deep learning and digital archaeology approach for mosquito repellent discovery

Insect-borne diseases kill >0.5 million people annually. Currently available repellents for personal or household protection are limited in their efficacy, applicability, and safety profile. Here, we describe a machine-learning-driven high-throughput method for the discovery of novel repellent molecules. To achieve this, we digitized a large, historic dataset containing [~]19,000 mosquito repellency measurements. We then trained a graph neural network (GNN) to map molecular structure and repellency. We applied this model to select 317 candidate molecules to test in parallelizable behavioral assays, quantifying repellency in multiple pest species and in follow-up trials with human volunteers. The GNN approach outperformed a chemoinformatic model and produced a hit rate that increased with training data size, suggesting that both model innovation and novel data collection were integral to predictive accuracy. We identified >10 molecules with repellency similar to or greater than the most widely used repellents. We analyzed the neural responses from the mosquito antennal (olfactory) lobe to selected repellents and found a limited correlation between these responses and our GNN representation. This approach enables computational screening of billions of possible molecules to identify empirically tractable numbers of candidate repellents, leading to accelerated progress towards solving a global health challenge.

bioengineering↗

bioRxiv2022

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors. One-Sentence SummaryAn odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.

neuroscience↗

bioRxiv2022

Metabolic activity organizes olfactory representations

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth, and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model1 for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (Principal Odor Map, POM), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences2 also follow smooth paths in POM despite large jumps in molecular structure. Just as the brains visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brains representation of the olfactory world.

neuroscience↗

bioRxiv2021

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. Author SummaryComputational models of neurons and their circuits are increasingly used by neuroscience researchers as a tool to probe fundamental aspects of brain function. Here we describe a database of computational models of neurons and networks that makes it easier to evaluate and reuse these models. The models in the database are available in a standard format, called NeuroML, that makes it easier to extend and reuse the models in simulation studies using a wide range of simulation software platforms. The use of this standard format also makes it easier to characterize models in an automated way and analyze relationships across the features of simulated data from model simulations. Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/459920v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@4be5adorg.highwire.dtl.DTLVardef@d3f138org.highwire.dtl.DTLVardef@81b8e2org.highwire.dtl.DTLVardef@9127bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

The perception of odor pleasantness is shared across cultures

Human sensory experience varies across the globe. Nonetheless, all humans share sensory systems with a common anatomical blueprint. In olfaction, it is unknown to what degree sensory perception, in particular the perception of odor pleasantness, is dictated by universal biological principles versus sculpted by culture. To address this issue, we asked 235 individuals from 9 diverse non-western cultures to rank the hedonic value of monomolecular odorants. We observed substantial global consistency, with molecular identity explaining 41% of the variance in individual pleasantness rankings, while culture explained only 6%. These rankings were predicted by the physicochemical properties of out-of-sample molecules and out-of-sample pleasantness ratings given by a separate group of industrialized western urbanites, indicating human olfactory perception is strongly constrained by universal principles.

animal behavior and cognition↗

bioRxiv2020

Drawing the Borders of Olfactory Space

In studies of vision and audition, stimuli can be chosen to span the visible or audible spectrum; in olfaction, the axes and boundaries defining the analogous odorous space are unknown. As a result, the population of olfactory space is likewise unknown, and anecdotal estimates of 10,000 odorants have endured. The journey a molecule must take to reach olfactory receptors (OR) and produce an odor percept suggests some chemical criteria for odorants: a molecule must be 1) volatile enough to enter the air phase, 2) non-volatile and hydrophilic enough to sorb into the mucous layer coating the olfactory epithelium, 3) hydrophobic enough to enter an OR binding pocket, and 4) activate at least one OR. Here, we developed a simple and interpretable quantitative model that reliably predicts if a molecule is odorous or odorless based solely on the first three criteria. Applying our model to a database of all possible small organic molecules, we estimate that over 30 billion possible compounds are odorous, 6 orders of magnitude larger than current estimates of 10,000. With this model in hand, we can define the boundaries of olfactory space in terms of molecular volatility and hydrophobicity, enabling representative sampling of olfactory stimulus space.

neuroscience↗

bioRxiv2020

Deconstructing the mouse olfactory percept through an olfactory ethological atlas

Odor perception in non-humans is poorly understood. Here, we generated the most comprehensive murine olfactory ethological atlas to date, consisting of behavioral responses to a diverse panel of 73 odorants, including 12 at multiple concentrations. These data revealed that the mouse behavior is incredibly diverse, and changes in response to odor identity and intensity. Using only behavioral responses, ~30% of the 73 odorants could be identified with high accuracy (>96%) by a trained classifier. Mouse behavior occupied a low-dimensional space, consistent with analyses of human olfactory perception. While mouse olfactory behavior is difficult to predict from the corresponding human olfactory percept, three fundamental properties are shared: odor valence is the primary axis of olfactory perception; the physicochemical properties of odorants can predict the olfactory percept; and odorant concentration quantitatively and qualitatively impacts olfactory perception. These results provide a template for future comparative studies of olfactory percepts among species.

neuroscience↗

bioRxiv2019

Severe Hyposmia Distinguishes Neuropathologically Confirmed Dementia with Lewy Bodies from Alzheimer’s Disease Dementia

Due to the absence of core clinical features, many subjects with neuropathologically-confirmed dementia with Lewy bodies (DLB) are never diagnosed as such during life. Most of these are diagnosed with Alzheimers disease dementia (ADD) or unspecified dementia. Unrecognized DLB therefore is a critical impediment to clinical studies and treatment trials of both ADD and DLB. There are numerous published studies that suggest that olfactory function tests may be able to differentiate some neurodegenerative conditions from each other and from normal subjects, but there are very few studies with neuropathological confirmation of diagnosis. We compared University of Pennsylvania Smell Identification Test (UPSIT) results in 209 subjects: 1) 29 concurrently meeting intermediate or high consensus clinicopathological criteria for both DLB and ADD 2) 96 meeting criteria for ADD without DLB 3) 84 control subjects that were non-demented and without parkinsonism at death. The DLB subjects had significantly lower (one-way ANOVA p < 0.0001, pairwise Bonferroni p < 0.05) first and mean UPSIT scores (13.7 and 13.2) than ADD (23.3 and 22.2) or controls (29.6 and 28.9). For subjects with first and mean UPSIT scores less than 20 and 17, respectively, Firth logistic regression analysis, adjusted for age, gender and mean MMSE score, conferred statistically significant odds ratios of 17.5 and 18.0 for predicting a DLB vs ADD diagnosis, as compared to 3.3 for the presence or absence of visual hallucinations throughout the clinical observation period. To our knowledge, this is the largest study to date comparing olfactory function in subjects with neuropathologically confirmed DLB and ADD. Olfactory function testing may be a convenient and inexpensive strategy for enriching dementia studies or clinical trials with DLB subjects, or conversely, reducing the inclusion of DLB subjects in ADD studies or trials.

neuroscience↗

bioRxiv2019

NeuronUnit: A package for data-driven validation of neuron models using SciUnit

Validating a quantitative scientific model requires comparing its predictions against many experimental observations, ideally from many labs, using transparent, robust, statistical comparisons. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most conscientious scientists. Thus the merits and limitations of existing models, or whether a new model is an improvement on the state-of-the-art, is often unclear.\n\nSoftware engineers seeking to verify, validate and contribute to a complex software project rely on suites of simple executable tests, called \"unit tests\". Drawing inspiration from this practice, we previously developed SciUnit, an easy-to-use framework for developing data-driven \"model validation tests\" - executable functions, here written in Python. Each such test generates and statistically validates predictions from a model against one relevant feature of empirical data to produce a score indicating agreement between the model and the data. Suites of such validation tests can be used to clearly identify the merits and limitations of existing models and developmental progress on new models.\n\nHere we describe NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support the validation of single-neuron models using data gathered by neurophysiologists and neuroanatomists. NeuronUnit integrates with existing technologies like Jupyter, Pandas, NeuroML and resources such as NeuroElectro, The Allen Institute, and The Human Brain Project in order to make neuron model validation as easy as possible for computational neuroscientists.

neuroscience↗

bioRxiv2019

Experience-dependent tuning of early olfactory processing in the adult brain

Experience-dependent plasticity in the central nervous system allows an animal to adaptively change their responses to stimuli over different time scales. In this study we explored the different time frames and mechanisms over which olfactory experience-dependent plasticity works. We measured the impact of experience on early olfactory processing by comparing naturally foraging animals with a diverse olfactory experience to animals from the same cohort that experienced a chronic reduction in adult olfactory experience. We placed age-matched sets of full-sister honey bees, Apis mellifera, into two different olfactory conditions, in which animals were allowed to forage ad libitum. In one condition (T), we reduced the olfactory experience of foraging bees by placing them in a tent in which both sucrose and pollen resources were associated with a single odor. In the second condition (F), bees were allowed to forage freely and therefore receive a diversity of naturally occurring resource-associated olfactory experiences. We found that bees with a reduced olfactory experience had less developed antennal lobes when compared to experienced foragers, suggesting early-adult sensory experience influences the development of olfactory processing. We next measured the antennal lobe glomerular responses to odors using calcium imaging, and found that diverse olfactory experience of bees also enhances the inter-individual variation in the glomerular response profiles to odors. Last, we measured the impact of this treatment in an olfactory learning assay. We found that bees with a reduced olfactory experience had more difficulty picking an odor out of a mixture, which led them to generalize more (or respond similarly) to different mixture components than bees with richer olfactory experiences. Our study highlights the impact of individual experience at multiple levels (i.e., behavioral, physiological, developmental) on early olfactory processing.

neuroscience↗

bioRxiv2018

Molecular Nearest Neighbors Determine Generalization of Odorant Mixtures

Most natural odors arise from mixtures of multiple odorants. Some such mixtures are perceived \"elementally\", with each odorant component clearly identifiable, while others are perceived \"configurally\", with the mixture adopting a perceptual quality distinct from any of the components. While the perceptual similarity of two mixtures is presumably related in some way to the similarity of the corresponding components, given the elemental/configural dichotomy it is unclear if any formal principle can be used to predict mixture similarity. To investigate this problem, we trained mice to respond to a binary reference mixture of structurally similar odorants (S+) and then tested generalization of this response to other structurally related binary test mixtures. Across 5 experiments, we parametrically varied these mixtures in distinct ways to test candidate models for the perceptual similarity of mixtures. The best-performing model predicted behavioral responses by considering, for each component of the S+, only the similarity of the most structurally similar (\"nearest neighbor\") component of each test mixture. We conclude that for mixture generalization tasks the olfactory system may deemphasize or discard information about mixture components not perceptually \"near\" enough to any of those in the S+, consistent with a sparse and elemental rule for perception of structurally-related binary mixtures.

neuroscience↗

bioRxiv2017

SwarmSight: Real-Time Tracking of Insect Antenna Movements and Proboscis Extension Reflex using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames-per-second videos, can capture antennal dynamics up to 15 Hz. We used the software to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

animal behavior and cognition↗

bioRxiv2016

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience↗