This is a repository to reproduce results for the work "Resolving artefacts in voltage-clamp experiments with computational modelling: an application to fast sodium current recordings".
To run the code, run pip install -r requirements.txt to install all the necessary dependencies. Python >3.6 and SUNDIALS are required (tested on Python 3.9 and 3.11, and SUNDIALS 7.2.1).
- Figure 3C: Change directory to
srcand runpython simulate-mc4auto-minimum.py nav. - Figure 4C: Change directory to
srcand runpython compensation-level-sweeps.py. - Figure 5C: Run
python figure-plot.py -m iyer -p NaIV_35C_80CP -d cell6 -l 90. - Figure 6A: Change directory to
srcand runpython averaging-issue.py. - Figure 6B: Change directory to
srcand run Jupyter notebookmutant-issue.ipynb. - Figure 7: Change directory to
src/apand run Jupyter notebookap-effects.ipynb. - Figure 8: Run
python figure-plot-diff-models.py -m gray -p NaIV_35C_80CP -d cell9 -l 90.
data: Experimental data (see below for how to download this).methods: Main python codes.models: Myokit models.protocols: Voltage clamp protocols.src: Other source code for studying experimental artefact effects.fit.py: Run model fitting to produce results inresults.
Experimental data of this study may be downloaded from the following link: https://doi.org/10.6084/m9.figshare.27193878.
The whole dataset should be placed within this repository as data such that data can be loaded and read properly.
- time in [ms]
- voltage in [mV]
- current in [pA]
- capacitance in [pF]
- resistance in [GOhm]
If you publish any work based on the contents of this repository please cite (CITATION file):
Chon Lok Lei, Alexander P. Clark, Michael Clerx, Siyu Wei, Meye Bloothooft, Teun P. de Boer, David J. Christini, Trine Krogh-Madsen, Gary R. Mirams. (2024). Resolving artefacts in voltage-clamp experiments with computational modelling: an application to fast sodium current recordings. bioRxiv, 2024.07.23.604780.