A state-mutating genetic algorithm to design ion-channel models.
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.
Menon
Vilas
V
Engineering Sciences and Applied Mathematics, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA.
Spruston
Nelson
N
Kath
William L
WL
D000465:Algorithms; D002462:Cell Membrane / Q000378:metabolism; D007473:Ion Channels / Q000737:chemistry* / Q000235:genetics*; D008962:Models, Theoretical; D009154:Mutation; D009474:Neurons / Q000502:physiology; D015222:Sodium Channels / Q000737:chemistry / Q000235:genetics
D007473:Ion Channels; D015222:Sodium Channels