Altered chloride ion channel kinetics associated with the ΔF508 cystic fibrosis mutation

Nature ◽  
1991 ◽  
Vol 354 (6354) ◽  
pp. 526-528 ◽  
Author(s):  
Wilfried Dalemans ◽  
Pascal Barbry ◽  
Guy Champigny ◽  
Sophie Jallat ◽  
Sophie Jallat ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Ion Channel ◽  
Chloride Ion ◽  
Channel Kinetics ◽  
Cystic Fibrosis Mutation ◽  
Ion Channel Kinetics

Memory in Ion Channel Kinetics

2021 ◽  
Author(s):  
M. P. Silva ◽  
C. G. Rodrigues ◽  
W. A. Varanda ◽  
R. A. Nogueira
Keyword(s):  
Ion Channel ◽  
Channel Kinetics ◽  
Ion Channel Kinetics

Halothane and isoflurane alter acetylcholine activated ion channel kinetics

1989 ◽  
Vol 173 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Martin D. Sokoll ◽  
Loyd R. Davies ◽  
Bula Bhattacharyya ◽  
Daniel Q. Zwagerman
Keyword(s):  
Ion Channel ◽  
Channel Kinetics ◽  
Ion Channel Kinetics

scholarly journals Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics

2017 ◽  
Author(s):  
Kylie A. Beattie ◽  
Adam P. Hill ◽  
Rémi Bardenet ◽  
Yi Cui ◽  
Jamie I. Vandenberg ◽  
...  
Keyword(s):  
Ion Channel ◽  
Voltage Clamp ◽  
Ion Current ◽  
Ion Currents ◽  
Channel Kinetics ◽  
Sinusoidal Voltage ◽  
Square Wave ◽  
Voltage Clamps ◽  
Ion Channel Kinetics ◽  
Cell Cell

AbstractUnderstanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics — the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fit a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells over-expressing hERG1a. The model is then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprised of a collection of physiologically-relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches.Table of Contents CategoryTechniques for Physiology1Key PointsIon current kinetics are commonly represented by current-voltage relationships, time-constant voltage relationships, and subsequently mathematical models fitted to these. These experiments take substantial time which means they are rarely performed in the same cell.Rather than traditional square-wave voltage clamps, we fit a model to the current evoked by a novel sum-of-sinusoids voltage clamp that is only 8 seconds long.Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions.The new model predicts the current under traditional square-wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically-relevant series of action potential clamps is predicted extremely well.The short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell-cell variability in current kinetics for the first time.

Synapse Models for Neural Networks: From Ion Channel Kinetics to Multiplicative Coefficient wij

1995 ◽  
Vol 7 (4) ◽  
pp. 713-734 ◽  
Author(s):  
François Chapeau-Blondeau ◽  
Nicolas Chambet
Keyword(s):  
Neural Networks ◽  
Ion Channel ◽  
Neural Systems ◽  
Channel Kinetics ◽  
Modeling Process ◽  
Multiplicative Coefficient ◽  
Ion Channel Kinetics ◽  
Synaptic Coefficient ◽  
Synapse Model ◽  
Different Levels

This paper relates different levels at which the modeling of synaptic transmission can be grounded in neural networks: the level of ion channel kinetics, the level of synaptic conductance dynamics, and the level of a scalar synaptic coefficient. The important assumptions to reduce a synapse model from one level to the next are explicitly exhibited. This coherent progression provides control on what is discarded and what is retained in the modeling process, and is useful to appreciate the significance and limitations of the resulting neural networks. This methodic simplification terminates with a scalar synaptic efficacy as it is very often used in neural networks, but here its conditions of validity are explicitly displayed. This scalar synapse also comes with an expression that directly relates it to basic quantities of synaptic functioning, and it can be endowed with meaningful physical units and realistic numerical values. In addition, it is shown that the scalar synapse does not receive the same expression in neural networks operating with spikes or with firing rates. These coherent modeling elements can help to improve, adjust, and refine the investigation of neural systems and their remarkable collective properties for information processing.

Linalool modifies the nicotinic receptor–ion channel kinetics at the mouse neuromuscular junction

2000 ◽  
Vol 42 (2) ◽  
pp. 177-181 ◽  
Author(s):  
L. Re ◽  
S. Barocci ◽  
S. Sonnino ◽  
A. Mencarelli ◽  
C. Vivani ◽  
...  
Keyword(s):  
Neuromuscular Junction ◽  
Ion Channel ◽  
Nicotinic Receptor ◽  
Channel Kinetics ◽  
Ion Channel Kinetics

scholarly journals Simulating complex ion channel kinetics with IonChannelLab

Channels ◽  
2010 ◽  
Vol 4 (5) ◽  
pp. 422-428 ◽  
Author(s):  
Jose A. De Santiago-Castillo ◽  
Manuel Covarrubias ◽  
Jorge E. Sánchez-Rodríguez ◽  
Patricia Perez-Cornejo ◽  
Jorge Arreola
Keyword(s):  
Ion Channel ◽  
Channel Kinetics ◽  
Ion Channel Kinetics ◽  
Complex Ion
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