Abstract
Ion channel kinetics are often modeled and simulated using Markov models, where rate constants provide the probability of transitioning between a defined connectivity of closed and open states. Usually, the connectivity and state identities (open versus closed) are set by the modeler and an optimization routine is used to search for the rate constants with which the model best matches the experimental activity. Here we present a novel approach for ion channel model specification where a genetic algorithm (GA) is used to optimize both the rate constants between state transitions as well as the identities of the states.
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