ragp

Model Documentation: Biophysical Modelling of Neuronal Phenotypes of the Right Atrial Ganglionic Plexus from Single Neuron Transcriptomics

Context of use: Simulation of single compartment neuron electrical activity in the RAGP.

Instructions for reproducing figures using data generated by models:

Clone GitHub repository and checkout to tag-v2.0.6 git clone https://github.com/Daniel-Baugh-Institute/BiophysicalModelingOfIntrinsicCardiacNeurons.git

A. Figures 2 and 3

1. Figure 2: Execute ct_genes.py, which reads data from threshold.py
2. Figure 3A: Run Tdata_plots.py, which reads the data from tdata_all_15.csv. Also run Tdata_plots_thresholds.py for plots showing the alternative Ct thresholds tested.
3. Figure 3B and 3C: Run Tdata_plots_thresholds.py. Plots for Ct thresholds of 14 and 16 can also be generated using this script by switching out the csv file names.

B. Figure 4

1. In code/IC_comp run CaTplot.py and Naplot.py

C. Figures 5 and 6

1. Figure 5A: i. Read data file 22aug25b_allData.json ii. Execute the 'classification' function in scAnalysis.py iii. Download classification_15.json available on the SPARC portal (https://doi.org/10.26275/cy9w-ttjn) and classification_firing_sequence.py iv. Run classification_firing_sequence.py (make sure that anything that is not a function in scAnalysis.py is commented out or it will run that too)
2. Figure 5B and 5C: To produce the distribution of firing types at different thresholds, use analysis_ct.py. The dataFolder, batchLabel, paramFile, and filename must be updated. For example for a Ct threshold of 13, use dataFolder = "24jan24_13", batchLabel = '24jan24_13', paramFile = 'params_13.csv', filename = 'your_working_directory_path//24jan24_13_allData.json'. Make sure that anything in scAnalysis.py that is not part of a function is commented out or it will run that too)
3. Figure 6: Use analysis_ct.py but switch out the file paths to be filename = '25jan24_scn1a_-20_allData.json' and filename = '25jan24_scn1a_allData+20.json' for a negative 20% change and positive 20% change in h_inf. Note that these files are available on the SPARC portal (https://doi.org/10.26275/cy9w-ttjn). Use paramFile = 'params.csv', dataFolder = '25jan24_scn1a_-20' and batchLabel='25jan24_scn1a_-20'. You will need to comment out any part of scAnalysis.py and classification_firing_sequence.py that is not a function (at the end of the script).

Note that a large amount of RAM is required to run this file

D. Figure 7

1. Read data file 22aug25b_allData.json
2. Execute the 'IV' function in scAnalysis.py
   Note that a large amount of RAM is required to run this file

E. Figure 8

1. Raw transcriptomic data and calculations for fold change difference in expression between ion channel genes can be found in primary/RAGP_4subs_raw_Ct_analyzed_Differential_expression.xlsx. The figure is also provided in the spreadsheet.

Instructions for running the models: Detailed instructions for running the model in NetPyNE and on the O2S2PARC platform are as follows:

I. NetPyNE implementation

1. Open a new Terminal window

2. Make a new directory into which to clone the repository

3. Clone GitHub repository and checkout to tag-v2.0.2 git clone https://github.com/suny-downstate-medical-center/ragp.git

4. Install NEURON and NetPyNE: pip3 install NEURON / pip install NEURON pip3 install netpyne / pip3 install netpyne

5. Compile mod files: nrnivmodl mod

6. On successful compilation, x86_64 folder is created

7. In order to run a single simulation, execute the following command: nrniv -python init.py

8. In order to run a batch simulation, MPI installation is mandatory: mpiexec --oversubscribe -np 32 nrniv -python -mpi batch.py

   a. Once the simulations are complete, run scAnalysis.py ipython -i scAnalysis.py

   b. Create _allData.json file by running the function readBatchData(dataFolder, batchLabel, paramFile = 'params.csv')

   c. Load the _allData.json file by running the function readAllData(jsonfilename)

   d. Analyse the data in _allData.json using the functions in scAnalysis.py

9. In order to run sobol sampling on the parameter space, execute sobol.py, following which a csv file (default name: params.csv) is generated that will contain the parameter values

10. In order to plot the binarized transcriptomics data, execute ipython Tdata_plots.py

11. In order to create the bar plot that compares Ct values, execute ipython ct_genes.py

II. O2S2PARC implementation:

1. Login to https://osparc.io/

2. Use the Jupyter Octave+Python Math service from the Dashboard

3. Clone the repository from GitHub checkout to tag-v2.0.2: https://github.com/suny-downstate-medical-center/ragp.git

4. Launch Terminal

5. Type the following commands on the Terminal

   a. cd work/ragp

   b. pip install --quiet NEURON

   c. pip install --quiet netpyne

6. Resume the steps from I.5.

Additional analysis:

Effect of varying phi_kcnc1 parameter on firing frequency (see generated plot in docs folder)

1. Use scAnalysis.py to run readAllData('22aug25c_allData.json') then fphi(df)