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A state-mutating genetic algorithm to design ion-channel models
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DOI：10.1073/pnas.0903766106.png)
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摘要
En 中文
Realistic computational models of single neurons require component ion channels that reproduce experimental findings. Here, a topology-mutating genetic algorithm that searches for the best state diagram and transition-rate parameters to model macroscopic ion-channel behavior is described. Important features of the algorithm include a topology-altering strategy, automatic satisfaction of equilibrium constraints (microscopic reversibility), and multiple-protocol fitting using sequential goal programming rather than explicit weighting. Application of this genetic algorithm to design a sodium-channel model exhibiting both fast and prolonged inactivation yields a six-state model that produces realistic activity-dependent attenuation of action-potential backpropagation in current-clamp simulations of a CA1 pyramidal neuron.
Keyword:
ACTION-POTENTIALS
SINGLE-CHANNEL
SLOW INACTIVATION
NA+ CHANNELS
SODIUM
ATTENUATION
CURRENTS
NEURONS
DENDRITES
KINETICS
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期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
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