Biosynthetic infochemical communication

Binary mixture quantification using cell-based odor biosensor system with active sensing

Organisms perceive odorants in the environment through the use of a large number of olfactory receptors. Various odor biosensors have been researched and developed in order to mimic this olfactory mechanism. This study examines the quantification of odorant concentrations through the use of a sensor array comprised of several types of cell-based odor sensors expressing insect olfactory receptors with nonlinear characteristics. The sensor system utilized an active sensing method in order to compare the responses of a target odorant and a prepared odorant in determining the relative concentration of the target odorant. By combining an active sensing method with a real-time reference method in which the target odorant was measured every time the prepared odorant was measured, the relative concentrations were successfully determined even when the response fluctuation was large or odorant sensor cell responses varied as measurement time increased. For proof of concept purposes, the study primarily focused on quantifying odorant concentrations composed of one or two odorant components. It was confirmed that an algorithm to find the optimal relative odorant concentration among a limited number of odorant concentrations is achievable. Though this study is still in the initial stage of the developing odor sensors and has many challenges, it can provide insight into paving the way towards a new type of odor biosensor with active sensing.

A Functional Agonist of Insect Olfactory Receptors: Behavior, Physiology and Structure

Chemical signaling is ubiquitous and employs a variety of receptor types to detect the cacophony of molecules relevant for each living organism. Insects, our most diverse taxon, have evolved unique olfactory receptors with as little as 10% sequence identity between receptor types. We have identified a promiscuous volatile, 2-methyltetrahydro-3-furanone (coffee furanone), that elicits chemosensory and behavioral activity across multiple insect orders and receptors. In vivo and in vitro physiology showed that coffee furanone was detected by roughly 80% of the recorded neurons expressing the insect-specific olfactory receptor complex in the antenna of Drosophila melanogaster, at concentrations similar to other known, and less promiscuous, ligands. Neurons expressing specialized receptors, other chemoreceptor types, or mutants lacking the complex entirely did not respond to this compound. This indicates that coffee furanone is a promiscuous ligand for the insect olfactory receptor complex itself and did not induce non-specific cellular responses. In addition, we present homology modeling and docking studies with selected olfactory receptors that suggest conserved interaction regions for both coffee furanone and known ligands. Apart from its physiological activity, this known food additive elicits a behavioral response for several insects, including mosquitoes, flies, and cockroaches. A broad-scale behaviorally active molecule non-toxic to humans thus has significant implications for health and agriculture. Coffee furanone serves as a unique tool to unlock molecular, physiological, and behavioral relationships across this diverse receptor family and animal taxa.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.

Ion Channel Based Bio-Synthetic Modulator for Diffusive Molecular Communication

In diffusion-based molecular communication (DMC), a transmitter nanomachine is responsible for signal modulation. Thereby, the transmitter has to be able to control the release of the signaling molecules employed for representing the transmitted information. In nature, an important class of control mechanisms for releasing molecules from cells utilizes ion channels which are pore-forming proteins across the cell membrane. The opening and closing of the ion channels is controlled by a gating parameter. In this paper, an ion channel based modulator for DMC is proposed which controls the rate of molecule release from the transmitter by modulating a gating parameter signal. Exploiting the capabilities of the proposed modulator, an on-off keying modulation technique is introduced and the corresponding average modulated signal, i.e., the average release rate of the molecules from the transmitter, is analyzed. However, since the modulated signal is random in nature, it may deviate from its average. Therefore, the concept of modulator noise is introduced and the statistics of the modulated signal are investigated. Finally, by assuming a simple transparent receiver, the performance of the proposed on-off keying modulation format is studied. The derived analytical expressions for the average modulated signal are confirmed with particle based simulations. Our numerical results reveal that performance estimates of DMC systems obtained based on the assumption of instantaneous molecule release at the transmitter may substantially deviate from the performance achieved with practical modulators.