Animals have evolved an incredible diversity of sensory systems to extract information from the environment. Of these, the chemosensory systems allow them to extract information from their chemical environments, so that behavioral preferences are elicited in response to stimuli that may be aversive or attractive. Animals live in complex environments where an infinite variety of chemical molecules may be encountered. These may be present as single chemicals, or as complex mixtures, where the relative concentrations of individual components differ. The tasks commonly carried out by the olfactory system include detection of odors, estimating their strength, identifying their source, and recognizing a specific odor in the background of another. The olfactory system in mammals is involved in physiological regulation, emotional responses (e.g., anxiety, fear, pleasure), reproductive functions (e.g., sexual and maternal behaviors), and social behaviors (e.g., recognition of members of the same species, family, clan, or outsiders). In insects such as the honeybee, it has been shown that scents modify behaviors associated with mating, foraging, recognition of kin, brood care, swarming, alarm, and defense (Reinhard and Srinivasanand 2009). Figure 1.1 shows a diagram of the olfactory epithelium of a mammal. Olfactory receptor neurons are bipolar, and from the apical side, cilia containing membrane-bound olfactory receptor proteins lie in an aqueous environment (mucus) overlying the epithelium. Odorant molecules need to partition from air into water before they can reach the transduction sites in the epithelium. Soluble odorant binding proteins are secreted into the aqueous mucus layer, and these may have an odorant carrier and preconcentration role. Over the last century, ideas that several classes of olfactory receptors exist, selective to chemical species on the basis of molecular size, shape, and charge, were based on evidence from chemistry (Beets 1978), olfactory psychophysics, and structure-activity relationships of odorants (Boelens 1974), together with the examination of “specific anosmias” in the human population, which all supported the definition of selectivity and specificity of putative olfactory receptors initiated by Amoore (1962a, 1962b, 1967). These were confirmed by developments in olfactory neurobiology and molecular genetics (Buck and Axel 1991; Buck 1997a, 1997b; Chess et al. 1992; Mombaerts et al. 1996a). The ideas that several classes of olfactory receptors exist, selective to chemical species on the basis of molecular size, shape, and charge, also pointed to individual olfactory receptors being rather broad in their selectivity to molecules within certain classes. The important molecular parameters of an odorant determining the olfactory response would include the adsorption and desorption energies of the molecule from air to a receptor interface, partition coefficients, and electron donor-acceptor interactions, depending on the polarizability of the molecule, and its molecular size and shape. The plethora of chemicals that an animal can sense, as well as their combinatorial and temporal variability, has made it difficult to understand how the brain processes the incoming information so that an animal can make sense of its chemical environment. Polak (1973) proposed a multiple profile–multiple receptor site model for vertebrate olfaction anticipating some of the combinatorial coding mechanisms later discovered. The identification of odorant receptor (OR) genes in rodents (Buck and Axel 1991), in Caenorhabditis elegans (Sengupta et al. 1996), and in Drosophila melanogaster (Clyne et al. 1999; Gao and Chess 1999) have given us a fundamental understanding of olfactory coding, especially at the olfactory receptor neuron (ORN) level. Individual ORs are proteins that traverse the cell membrane of the cilia of the olfactory neuron. It appears that there may be hundreds of odorant receptors, but only one (or at most a few) expressed in each olfactory receptor neuron. These families of proteins may be encoded by as many as 1000 different genes in humans. This is a large number and accounts for about 2% of the human genome. In humans, however, most are inactive pseudogenes, and only around 350 code for functional receptors. There are many more functional genes in macrosmatic animals like rats. These receptor proteins are members of a well-known receptor family called the seven-transmembrane domain G-protein-coupled receptors (GPCRs) (see Figure 1.2). The hydrophobic regions (the transmembrane parts) contain maximum sequence homology to other members of the G-protein-linked receptor family. There are some notable features of these olfactory receptors, like the divergence in sequence in the third, fourth, and fifth transmembrane domains, that suggest how a large number of different odorants may be discriminated (Pilpel and Lancet 1999). As crystallographic information on olfactory receptors is lacking, they have been modeled based on their resemblance to rhodopsin. Gelis et al. (2012) has published models of putative binding sites of some human olfactory receptors. On the inner side of the cell membrane, proteins called G-proteins are associated with olfactory receptor. These bind the guanine nucleotides—guanine triphosphate and guanine diphosphate. They are made up of three subunits and are located with the inner surface of the plasma membrane. They are closely associated with the transmembrane receptor protein. When an odorant binds, it is thought that an allosteric change in conformation occurs, in turn causing a conformational change in a subunit of the G-protein Gα—displacing bound guanine triphosphate (GTP) and allowing it to bind GTP. This in turn produces an activated subunit that dissociates from the other subunits and activates another effector molecule, triggering a cascade of events that leads to the opening of an ion channel, and change of electrical potential across the cell membrane. As this electrical potential propagates to the basal side of the cell, it triggers in turn voltage-gated ion channels so that a series of electrical spikes results, which are transmitted to the processing centers in the brain via the axon of the olfactory neuron. Our understanding is that mammalian and insect olfactory systems are combinatorial in nature—instead of activating a single specialized receptor, each chemical stimulus induces a complex pattern of responses across the olfactory receptor array. The investigation of OR expression patterns has made it possible to dissect the major circuits underlying olfaction (Hoare et al. 2011; Imai et al. 2010; Leinwand and Chalasani 2011; Ressler 1994; Ressler et al. 1993; Su et al. 2009; Vassar et al. 1993). The evidence obtained confirmed previous concepts of a common design of mammalian and insect olfactory systems that are discussed by Hildebrand and coworkers (Hildebrand and Shepherd 1997; Hildebrand 2001; Martin et al. 2011). The consequence of the combinatorial design of the olfactory system is that the number of unique odor representations is not limited to the number of different receptor types, but can be estimated as mn, where n corresponds to the number of receptor types available and m the number of possible response states that each sensor can assume. This is limited to the available signal-to-noise parameters associated with the working system (Cleland and Linster 2005). Vertebrate or invertebrate life surviving in complex, changing environments requires the use of sophisticated sensory systems to detect, classify, and interpret patterns of input stimulation. Coding mechanisms by which a certain pattern of stimulations may be described are inherent. Such codes may be defined as sets of symbols that can be used to represent patterns of organizations and the sets of rules that govern the selection and use of these symbols. Sensory coding mechanisms in biological systems would appear to project some representation of sensory inputs as a pattern at a high level of the nervous system, the neural activity resulting being then related to the previous experience with regard to this pattern or associated patterns. Fundamental concepts of pattern classification that seem to be common in biological systems would appear to be template matching, whereby the pattern to be classified is compared with a set of templates, one for each class, the closest match determining the classification, and feature detection systems, in which a number of measurements are taken on the input pattern and the resulting data are combined to reach a decision. These systems may involve either a sequential approach whereby information from the evaluation of some features is used to decide which features to evaluate next, or a parallel approach where information about all features is evaluated at the same time with no weight being placed on any particular feature. The remarkable capabilities of the biological chemosensory systems in detection, recognition, and discrimination of complex mixtures of chemicals, together with rapid advances in understanding how these systems operate, have stimulated the imagination and interest of many researchers and commercial organizations for the development of electronic analogs. The dream of emulating biological olfaction using artificial devices was conceptually realized by Persaud and Dodd (1982), who demonstrated that an array of electronic chemical sensors with partial specificity could be used to discriminate between simple and complex odors; i.e., the combinatorial aspects of olfactory receptors could be emulated, and this could achieve remarkable flexibility in terms of the numbers of types of analytes that can be discriminated. This led to a burgeoning of the “electronic nose” field of research, and formation of many commercial enterprises interested in exploiting a wide range of applications, including environmental, food, medical, security, and others. The researchers and companies have produced instruments that combine gas sensor arrays and pattern analysis techniques for the detection, identification, or quantification of volatile compounds. The multivariate response of an array of chemical gas sensors with broad and partially overlapping selectivities can be processed as a pattern or “fingerprint” to discriminate a wide range of odors or volatile compounds using pattern recognition algorithms. The instruments typically consist of a gas sensor array comprising many types of sensing technologies, a sample delivery system, and the appropriate electronics for signal processing, data acquisition, and storage. Processing of data from such systems can be split into four sequential stages: signal preprocessing, dimensionality reduction, prediction, and validation. The numbers of sensors incorporated into the devices are relatively small, and the data handling approaches have been based on traditional chemometric or neural network methods for processing multivariate data. Applications using such chemosensory arrays at present involve issues such as sensor drift, poor sensitivity compared to biological systems, and interference from background odors. With further understanding of biological processes, some of these engineering limitations may be reduced by the adaptation of biologically plausible models for signal processing. This chapter gives an introduction to biological chemoreception, going on to the field of artificial olfaction, and discussing some of the signal processing concepts that may be useful in mimicking biological olfactory systems.
© 2013 by Taylor & Francis Group, LLC.
Performance of a Computational Model of the Mammalian Olfactory System.In: Persaud KC, Marco S, Gutiérrez-Gálvez A, editors. Neuromorphic Olfaction. Boca Raton (FL): CRC Press/Taylor & Francis; 2013. Chapter 6. Neuromorphic Olfaction. 2013. PMID: 26042330 Free Books & Documents. Review.
The Synthetic Moth: A Neuromorphic Approach toward Artificial Olfaction in Robots.In: Persaud KC, Marco S, Gutiérrez-Gálvez A, editors. Neuromorphic Olfaction. Boca Raton (FL): CRC Press/Taylor & Francis; 2013. Chapter 4. Neuromorphic Olfaction. 2013. PMID: 26042327 Free Books & Documents. Review.
Influence of Cat Odor on Reproductive Behavior and Physiology in the House Mouse: (Mus Musculus).In: Mucignat-Caretta C, editor. Neurobiology of Chemical Communication. Boca Raton (FL): CRC Press/Taylor & Francis; 2014. Chapter 14. Neurobiology of Chemical Communication. 2014. PMID: 24830030 Free Books & Documents. Review.
Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. 2015. PMID: 26269925 Free Books & Documents. Review.
Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).Phys Biol. 2013 Aug;10(4):040301. doi: 10.1088/1478-3975/10/4/040301. Epub 2013 Aug 2. Phys Biol. 2013. PMID: 23912807