Bayesian inference of kinetic schemes for ion channels by Kalman filtering

  1. Jan L Münch  Is a corresponding author
  2. Fabian Paul
  3. Ralf Schmauder
  4. Klaus Benndorf  Is a corresponding author
  1. Friedrich Schiller University Jena, Germany
  2. University of Chicago, United States

Abstract

Inferring adequate kinetic schemes for ion channel gating from ensemble currents is a daunting task due to limited information in the data. We address this problem by using a parallelized Bayesian filter to specify hidden Markov models for current and fluorescence data. We demonstrate the flexibility of this algorithm by including different noise distributions. Our generalized Kalman filter outperforms both a classical Kalman filter and a rate equation approach when applied to patch-clamp data exhibiting realistic open-channel noise. The derived generalization also enables inclusion of orthogonal fluorescence data, making unidentifiable parameters identifiable and increasing the accuracy of the parameter estimates by an order of magnitude. By using Bayesian highest credibility volumes, we found that our approach, in contrast to the rate equation approach, yields a realistic uncertainty quantification. Furthermore, the Bayesian filter delivers negligibly biased estimates for a wider range of data quality. For some data sets it identifies more parameters than the rate equation approach. These results also demonstrate the power of assessing the validity of algorithms by Bayesian credibility volumes in general. Finally, we show that our Bayesian filter is more robust against errors induced by either analog filtering before analog-to-digital conversion or by limited time resolution of fluorescence data than a rate equation approach.

Data availability

We included the simulated data time traces into supporting files and we uploaded the source code on https://github.com/JanMuench/Tutorial_Patch-clamp_data and https://github.com/JanMuench/Tutorial_Bayesian_Filter_cPCF_data

The following data sets were generated

Article and author information

Author details

  1. Jan L Münch

    Institut für Physiologie II, Friedrich Schiller University Jena, Jena, Germany
    For correspondence
    jan.muench@med.uni-jena.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9177-6466
  2. Fabian Paul

    Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ralf Schmauder

    Institut für Physiologie II, Friedrich Schiller University Jena, Jena, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Klaus Benndorf

    Institut für Physiologie II, Friedrich Schiller University Jena, Jena, Germany
    For correspondence
    KLAUS.BENNDORF@med.uni-jena.de
    Competing interests
    The authors declare that no competing interests exist.

Funding

Deutsche Forschungsgemeinschaft (TRR 166 ReceptorLight (Project A5) of the)

  • Klaus Benndorf

Deutsche Forschungsgemeinschaft (Research Unit 2518 DynIon (Project P2))

  • Klaus Benndorf

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Marcel P Goldschen-Ohm, University of Texas at Austin, United States

Version history

  1. Preprint posted: April 28, 2020 (view preprint)
  2. Received: September 2, 2020
  3. Accepted: April 22, 2022
  4. Accepted Manuscript published: May 4, 2022 (version 1)
  5. Version of Record published: August 1, 2022 (version 2)
  6. Version of Record updated: February 9, 2024 (version 3)

Copyright

© 2022, Münch et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 997
    Page views
  • 262
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jan L Münch
  2. Fabian Paul
  3. Ralf Schmauder
  4. Klaus Benndorf
(2022)
Bayesian inference of kinetic schemes for ion channels by Kalman filtering
eLife 11:e62714.
https://doi.org/10.7554/eLife.62714

Share this article

https://doi.org/10.7554/eLife.62714

Further reading

    1. Computational and Systems Biology
    James D Brunner, Nicholas Chia
    Research Article

    The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.