![]() This paper uses examples from cardiac electrophysiology to discuss aspects related to parameter estimation, (i) Parameter unidentifiability (found in 9 out of 13 of the considered models) results in an inability to determine the correct layout of a model, contradicting the idea that model structure and parameters provide insights into underlying molecular processes, (ii) The information content of experimental voltage step clamp data is discussed, and a short but sufficient protocol for parameter estimation is presented, (iii) MMs have been associated with high computational cost (owing to their large number of state variables), presenting an obstacle for multicellular whole organ simulations as well as parameter estimation. They provide a versatile structure for modelling single channel data, gating currents, statedependent drug interaction data, exchanger and pump dynamics, etc. Markov models (MMs) represent a generalization of Hodgkin-Huxley models.
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