Abstract
Inferring the complex conformational dynamics of ion channels from ensemble currents is a daunting task due to limited information in the data leading to poorly determined model inference and selection. We address this problem with a parallelized Kalman filter for specifying Hidden Markov Models for current and fluorescence data. We demonstrate the flexibility of this Bayesian network by including different noises distributions. The accuracy of the parameter estimation is increased by tenfold compared to fitting Rate Equations. Furthermore, adding orthogonal fluorescence data increases the accuracy of the model parameters by up to two orders of magnitude. Additional prior information alleviates parameter unidenfiability for weakly informative data. We show that with Rate Equations a reliable detection of the true kinetic scheme requires cross validation. In contrast, our algorithm avoids overfitting by automatically switching of rates (continuous model expansion), by cross-validation, by applying the ‘widely applicable information criterion’ or variance-based model selection.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The new version contains some corrections and a feature box which explaines the statistical problems our algorithm is concerned with. The supplementary material is now in the appendix.