Estimation of Ionic Currents and Compensation Mechanisms from Recursive Piecewise Assimilation of Electrophysiological Data

The identification of ion channels expressed in neuronal function and neuronal dynamics is critical to understanding neurological disease. This program calls for advanced parameter estimation methods that infer ion channel properties from the electrical oscillations they induce across the cell membrane. The characterization of expressed ion channels would further facilitate the diagnosis of channelopathies and help select the best therapy for neurological or cardiac disease. Here, we describe Recursive Piecewise Data Assimilation (RPDA), as a computational method that successfully deconvolutes the ionic current waveforms of a hippocampal neuron from the assimilation of current-clamp recordings. The strength of this approach is to simultaneously estimate all ionic currents in the cell from a small but high-quality dataset. RPDA allows us to quantify collateral alterations in non-targeted ion channels that demonstrate the potential of the method as a drug toxicity counter-screen. The method is validated by estimating the selectivity and potency of known ion channel inhibitors in agreement with IC50.