Canal sodium

Résumé

Un canal sodium, ou sodique, est un canal ionique spécifique aux ions sodium. Il en existe de plusieurs types. Le premier à avoir été décrit est le canal sodique du potentiel d'action, responsable entre autres de la dépolarisation du neurone et du myocyte, de la propagation du signal nerveux et de la propagation de l'activation électrique du myocarde. thumb|Vue schématique du canal sodique La sous-unité Alpha constituée de quatre domaines et formant le pore central du canal ainsi que ses deux sous-unités béta Il faut différencier les canaux sodium stricts des canaux perméants aux cations, c’est-à-dire principalement sodium et potassium. À la différence de ces derniers, les canaux sodium sont imperméables aux ions potassium. Différents types de canaux sodium Système nerveux central Les cellules excitables du système nerveux central maintiennent de façon active (c’est-à-dire en consommant de l'énergie) une différence de potentiel d'environ −70 mV entre les milieu

Source officielle

https://fr.wikipedia.org/wiki/Canal_sodium

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Single Neuron Optimization as a Basis for Accurate Biophysical Modeling : The Case of Cerebellar Granule Cells

Stefano Masoli, Werner Alfons Hilda Van Geit

In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

Frontiers Media Sa2017

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Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Margherita Marchi, Stefano Zamuner

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.

NATURE RESEARCH2020

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Mutation and analysis of a photoactivated ion channel. Channelrhodopsin from Volvox Carteri

Florent Beyrière

EPFL2008

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