A Markov Model of Cell Membrane Potassium Channel and Prediction of Channel Status

Soheila Esmaeili P., Somayyeh Shahhosseini, S.H. Sabzposhan


The most cell electrophysiological models are not able to predict the channel state, which it can be very helpful in patient treatment. So in this research we intend to represent a model for predicting it based on Markov model for ion channel.  To obtain the data, we used a software environment consistent with the cellular conditions. Next step is estimation the model parameters. In order to achieve this goal, there are some sub-step. First, it is essential to specify channel states. In addition, it is used a method of linearization of channel macroscopic current for states Distinction.  After Distinction of different channel states, finding the stopping times in each state and calculating the model parameters by use of relations between continuous- time Markov systems is done. Then the probabilities of transfer from one state to another are calculated in terms of time and voltage.  Consequently, we could find probability matrix of state changes of Markov, which made it possible to predict the channel state in different voltages. The results obviously show the dependence of model parameters to the voltage of two sides of channel. This method of modeling is able to predict channel state in each voltage. Furthermore, the predictions of channel states for 100 future states are shown. Assessment criteria for accuracy of this model, is measured by comparison between actual channel conductance that is obtained from macroscopic current of the software in different voltages and conductance that is obtained from the considered model.


Model parameters, Status of channel, Probability matrix, Markov model.

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