Borisyuk A. Estimation of ion channel kinetic model parameters from macroscopic synaptic currents.

In this study a novel method based on a maximum likelihood approach is described that accurately estimates kinetic constants, unitary current and number of ion channels from macroscopic currents. The method accounts for correlations between different time points of macroscopic currents and utilizes the property of semiseparability of covariance matrix for computationally efficient estimation of macroscopic current likelihood and its gradient. The computational complexity of the proposed method scales linearly with the number of channel states as opposed to the cubic dependence in the most fast of the previously described methods. The developed approach allows to evaluate rate constants of kinetic models with very complex topologies. As opposite to other methods of this type, the new method is applicable to the analysis of postsynaptic currents and allows estimation of kinetic constants of synaptic receptors from experimentally feasible number of postsynaptic currents. Accuracy of unitary current estimates obtained with this method is noticeably improved as compared to the peak-scaled non-stationary fluctuation analysis. This leads to a possibility to precisely estimate this important parameter from a few postsynaptic currents that could be easily recorded in steady-state conditions. Keywords: maximum likelihood estimation, kinetic model, semiseparable matrix, synaptic current, GABAA receptor, unitary current, nonstationary fluctuation analysis.