Parameter unidentifiability was found in 9 out of 13 considered Markov models, and model stiffness increased computation time more than the number of states.
Parameter unidentifiability is common in Markov models of ion channels, and model stiffness is the primary driver of computational cost, highlighting the need for improved parameter estimation protocols.
Markov models (MMs) represent a generalization of Hodgkin-Huxley models. They provide a versatile structure for modelling single channel data, gating currents, state-dependent drug interaction data, exchanger and pump dynamics, etc. 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. It is shown that the stiffness of models increases computation time more than the number of states. (iv) Algorithms and software programs are provided for steady-state analysis, analytical solutions for voltage steps and numerical derivation of parameter identifiability. The results provide a new standard for ion channel modelling to further the automation of model development, the validation process and the predictive power of these models.
Fink et al. (2009) studied cardiac electrophysiology. Markov models was evaluated on Parameter unidentifiability. Parameter unidentifiability was found in 9 out of 13 considered Markov models, and model stiffness increased computation time more than the number of states.