Abstract
Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. Previously published experimental data of an illustrative ion channel (NaV1.5) are exploited to develop a step by step optimization of the two models in close comparison. A conflicting practical limitation is recognized for the HH model, which only supplies one parameter to model two distinct electrophysiological behaviours. In addition, a step by step procedure is provided to correctly optimize the kinetic Markov-type model. Simplified Markov-type kinetic models are currently the best option to closely approximate the known complexity of the macroscopic currents of ion channels. Their optimization can be achieved through a rationally guided procedure, and allows to obtain models with a computational burden that is comparable with HH models one.
Highlights
Nowadays, biologically inspired neural simulations, sustained by both escalating computational power and more detailed comprehension of the nervous system physiology[1], are becoming increasingly popular and appreciated, despite their concurrent boosting complexity[2,3,4,5,6,7]
Our study recognizes some critical limitations of the Hodgkin and Huxley (HH) formalism in modelling the known complexity of NaV1.5 macroscopic currents, and shows how a simplified kinetic Markov-type model is better suited to approximate in detail these currents
It provides a practical guide to a procedural optimization of simplified kinetic models, which are shown to exhibit a computational load comparable to that of the HH model
Summary
Biologically inspired neural simulations, sustained by both escalating computational power and more detailed comprehension of the nervous system physiology[1], are becoming increasingly popular and appreciated, despite their concurrent boosting complexity[2,3,4,5,6,7]. The modelled single cells in turn mainly and directly derive their electrophysiological properties (that is, the fundamentals of the entire modelled neural networks) from the kinetics of the macroscopic currents of different kinds of voltage-gated ion channels[8]. Phenomenological models of ion channels, constitute the building blocks of biologically inspired neuronal cells and neural networks models. Current electrophysiological techniques are able to provide huge amount of data with unprecedented details on voltage-gated ion channels. These techniques have been developed as patch-clamp methods[9] but, when used in whole-cell configuration, result more suitable for recording the macroscopic currents of ion channels, significantly increasing our comprehension of the kinetic features of ion channels. A gap in integrating the improved access to functional properties of ion channels into whole-cell models has been for long recognized[10,11]
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