Dynamical Modeling of the Moth Pheromone-Sensitive Olfactory Receptor Neuron within Its Sensillar Environment

In insects, olfactory receptor neurons (ORNs), surrounded with auxiliary cells and protected by a cuticular wall, form small discrete sensory organs – the sensilla. The moth pheromone-sensitive sensillum is a well studied example of hair-like sensillum that is favorable to both experimental and modeling investigations. The model presented takes into account both the molecular processes of ORNs, i.e. the biochemical reactions and ionic currents giving rise to the receptor potential, and the cellular organization and compartmentalization of the organ represented by an electrical circuit. The number of isopotential compartments needed to describe the long dendrite bearing pheromone receptors was determined. The transduction parameters that must be modified when the number of compartments is increased were identified. This model reproduces the amplitude and time course of the experimentally recorded receptor potential. A first complete version of the model was analyzed in response to pheromone pulses of various strengths. It provided a quantitative description of the spatial and temporal evolution of the pheromone-dependent conductances, currents and potentials along the outer dendrite and served to determine the contribution of the various steps in the cascade to its global sensitivity. A second simplified version of the model, utilizing a single depolarizing conductance and leak conductances for repolarizing the ORN, was derived from the first version. It served to analyze the effects on the sensory properties of varying the electrical parameters and the size of the main sensillum parts. The consequences of the results obtained on the still uncertain mechanisms of olfactory transduction in moth ORNs – involvement or not of G-proteins, role of chloride and potassium currents – are discussed as well as the optimality of the sensillum organization, the dependence of biochemical parameters on the neuron spatial extension and the respective contributions of the biochemical and electrical parameters to the overall neuron response

Electroplasma technologies: automation of the project life cycle

Efficiency of the automated methods of plasmatrons designing can be raised due to integration of designing and manufacture technologies. The basic directions of automation for plasmotron designing are considered. Models and forms of algorithmization for the automated designing in electroplasma technologies are presentedЭффективность автоматизированных методов проектирования плазмотронов можно повысить за счет интеграции технологий проектирования и производства. Рассмотрены основные направления автоматизации процедур проектирования плазмотронов. Представлены модели и формы алгоритмизации автоматизированного проектирования в электроплазменных технология

Computational Model of the Insect Pheromone Transduction Cascade

A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protein and effector enzyme activation—and focuses on the main post-effector processes. These processes involve the production and degradation of second messengers (IP3 and DAG), the opening and closing of a series of ionic channels (IP3-gated Ca2+ channel, DAG-gated cationic channel, Ca2+-gated Cl− channel, and Ca2+- and voltage-gated K+ channel), and Ca2+ extrusion mechanisms. The whole network is regulated by modulators (protein kinase C and Ca2+-calmodulin) that exert feedback inhibition on the effector and channels. The evolution in time of these linked chemical species and currents and the resulting membrane potentials in response to single pulse stimulation of various intensities were simulated. The unknown parameter values were fitted by comparison to the amplitude and temporal characteristics (rising and falling times) of the experimentally measured receptor potential at various pheromone doses. The model obtained captures the main features of the dose–response curves: the wide dynamic range of six decades with the same amplitudes as the experimental data, the short rising time, and the long falling time. It also reproduces the second messenger kinetics. It suggests that the two main types of depolarizing ionic channels play different roles at low and high pheromone concentrations; the DAG-gated cationic channel plays the major role for depolarization at low concentrations, and the Ca2+-gated Cl− channel plays the major role for depolarization at middle and high concentrations. Several testable predictions are proposed, and future developments are discussed

Modeling the cellular mechanisms and olfactory input underlying the triphasic response of moth pheromone-sensitive projection neurons

In the antennal lobe of the noctuid moth Agrotis ipsilon, most pheromone-sensitive projection neurons (PNs) exhibit a triphasic firing pattern of excitation (E-1)-inhibition (I)-excitation (E-2) in response to a pulse of the sex pheromone. To understand the mechanisms underlying this stereotypical discharge, we developed a biophysical model of a PN receiving inputs from olfactory receptor neurons (ORNs) via nicotinic cholinergic synapses. The ORN is modeled as an inhomogeneous Poisson process whose firing rate is a function of time and is fitted to extracellular data recorded in response to pheromone stimulations at various concentrations and durations. The PN model is based on the Hodgkin-Huxley formalism with realistic ionic currents whose parameters were derived from previous studies. Simulations revealed that the inhibitory phase I can be produced by a SK current (Ca2+-gated small conductance K+ current) and that the excitatory phase E-2 can result from the long-lasting response of the ORNs. Parameter analysis further revealed that the ending time of E-1 depends on some parameters of SK, Ca2+, nACh and Na+ currents; I duration mainly depends on the time constant of intracellular Ca2+ dynamics, conductance of Ca2+ currents and some parameters of nACh currents; The mean firing frequency of E-1 and E-2 depends differentially on the interaction of various currents. Thus it is likely that the interplay between PN intrinsic currents and feedforward synaptic currents are sufficient to generate the triphasic firing patterns observed in the noctuid moth A. ipsilon

Mean frequency response curves of the ORN population in response to different concentrations and durations of the pheromone stimulation.

<p>A. Response data curve (blue) and fitted curve (red) to stimulus: 10 ng and 200 ms. B. Fitted response curves to the same stimulation period 200 ms and different stimulation doses from 0.1 to 10 ng. C. Response data curve (blue) and fitted curve (red) to stimulus: 10 ng and 1000 ms. D. Fitted response curves to the same stimulation dose 10 ng and different stimulation periods from 100 to 1000 ms.</p

Synaptic and intrinsic currents in a PN from the simulation results shown in Fig 1.

<p>Left panel: the slow components of the repolarizing currents (black lines) and depolarizing currents (magenta lines); <i>I</i>_SK and-<i>I</i>_nACh (A), <i>I</i>_A and—<i>I</i>_Na (C) and <i>I</i>_Kd and—<i>I</i>_Ca (E). Right panel: Fast components of the repolarizing (black lines) and depolarizing currents (magenta lines); <i>I</i>_SK and <i>I</i>_nACh (B), <i>I</i>_A and <i>I</i>_Na (D) and <i>I</i>_Kd and <i>I</i>_Ca (F). Note that in the left panel we draw the minus values of <i>I</i>_nACh, <i>I</i>_Na, <i>I</i>_Ca for comparing their amplitudes, while in the right panel we draw the values of <i>I</i>_nACh, <i>I</i>_Na, <i>I</i>_Ca directly for comparing their depolarizing and repolarizing effects). The slow components of E₁ (from 5140 to 5770ms) and I (from 5770 to 6700ms) are enlarged in the insets in A, C and E; and the fast components of E₁ and the period transiting from E₁ to I (from 5770 to 6200ms) are enlarged in the insets in B, D and F.</p

Effects of <i>I</i>_Ca, <i>I</i>_SK and dynamics of intracellular Ca²⁺ on PN response characteristics.

<p>Top panel: effects of the mean maximal conductance <math><mrow><mi>g</mi></mrow><mo>-</mo></math>_SK of <i>I</i>_SK on E₁ and I durations (A) and mean firing frequency of E₁ and E₂ (B). Intermediate panel: effects of the mean maximal conductance <math><mrow><mi>g</mi></mrow><mo>-</mo></math>_Ca of <i>I</i>_Ca on E₁ and I duration (C) and mean firing frequency of E₁ and E₂ (D). Bottom panel: effects of time constant τ_<i>Ca</i> of Ca²⁺ dynamics on E₁ and I duration (E) and mean firing frequency of E₁ and E₂ phases (F).</p

Major effects of <i>I</i>_A on PN response characteristics.

<p>Top panel: effects of <math><mrow><mi>g</mi></mrow><mo>-</mo></math>_A on E₁ and I duration (A) and mean firing frequency of E₁ and E₂ (B). Intermediate panel: effects of <i>V</i>_0.5act on E₁ and I duration (C) and mean firing frequency of E₁ and E₂ phases (D). Bottom panel: effects of <i>S</i>_m on E₁ and I duration (E) and mean firing frequency of E₁ and E₂ phases (F).</p

Effects of stimulation parameters on PN response characteristics.

<p>Top panel: effects of stimulus duration on E₁ and I duration (A) and mean firing frequency of E₁ and E₂ (B). Bottom panel: effects of stimulus concentration on E₁ and I durations (C) and mean firing frequency of E₁ and E₂ phases (D).</p