Metabolic spiking

Tutorial

Please see the folder tutorial for getting started. Google collaboratory based Explore the basic spiking with metabolism: Tutorial1

Explore how spiking can provide RETROS protection: Tutorial2

Binder based, select individual file Tutorial.ipynb and Tutorial2.ipynb to explore the same files as above. (Currently facing slow loading times, please use above links for now.)

Launch tutorial1 by clicking :

Launch tutorial2 by clicking :

Installation and requirements

This software was tested on Ubuntu machine (20.04) with Anaconda python package manager. To install the exact python environment, with compatible libraries please use the py36.yml file as follows (you have to edit the last line of this file depending on where anaconda is installed on your machine)

 conda env create -f py36.yml

After this, please activate this specific python environment by

  user@computer:~/metabolic_spiking$ source activate py36
  (py36)user@computer:~/metabolic_spiking$

Generate figures from the paper submitted to PNAS for review

This is very similar to that submitted to bioarxiv but, the PD version is now in supps. So the figure numbering is messed up and will be corrected in the subsequent versions.

Figure in MS	.odg file /	.py file
Figure 1	/figure_odgs/Figure1.odg	fig1.py
Figure 2	/figure_odgs/Figure2.odg	fig2.py
Figure 3	/figure_odgs/Figure3.odg	fig2_v6_2.py
Figure 4	/figure_odgs/Figure4.odg	fig3_dfb.py
Figure 5	/figure_odgs/Figure5.odg	fig3_nw.py
Figure 6	/figure_odgs/Figure6.odg	fig_predictions.py
Figure S1	/figure_odgs/FigureS1.odg	fig1_supp.py
Figure S2	/figure_odgs/FigureS2.odg	fig1_supp_b.py
Figure S3	/figure_odgs/FigureS3.odg	fig2_supp.py
Figure S4	/figure_odgs/FigureS4.odg	fig3_nw_supp.py
Figure S5	/figure_odgs/FigureS5.odg	-NA-
Figure S6	/figure_odgs/FigureS6.odg	-NA-

Figures 1, S1 and S2 from the MS

Please run the steady_state.py first if you want to regenerate everthing from the beginning, you can skip this line if you want to use the pre-generated data.

   (py36)user@computer:~/metabolic_spiking$ python steady_state.py

This will refresh / re-populate the contents of these folders (i) ./reaction_rates/ and (ii) ./steady_state/ with .npz files, after this please type

   (py36)user@computer:~/metabolic_spiking$ python fig1.py
   (py36)user@computer:~/metabolic_spiking$ python fig1_supp.py
   (py36)user@computer:~/metabolic_spiking$ python fig1_supp_b.py

You will have Figure1.png, Figure1_supp.png, Figure1_supp_b.png in your folder after this.

Figure 2 and S3 from the MS

Please run ret_intrinsic_summary.py and fet_intrinsic_summary.py for starting from the beginning, you can skip this if you don't want to use the pre-generated data.

   (py36)user@computer:~/metabolic_spiking$ python ret_intrinsic_summary.py
   (py36)user@computer:~/metabolic_spiking$ python fet_intrinsic_summary.py

This will refresh / re-populate the contents of ./spike_compensation/ folder with some spike_compensate_summary_*.npz files, after this please type

   (py36)user@computer:~/metabolic_spiking$ python fig2.py
   (py36)user@computer:~/metabolic_spiking$ python fig2_supp.py

You will have Figure2.png and Figure2_supp.png in your folder after this. The second line with also add some additional files to the above folder

Figure 4 dfb neuron

Please run the following

   (py36)user@computer:~/metabolic_spiking$ python fig3_dfb.py

This will output Figure3_dfb.png file

Figure 5 network model

You have to run the model if you want for other seeds/parameter values than the ones shown in the figure.

   (py36)user@computer:~/metabolic_spiking$ python nw_va.py
   (py36)user@computer:~/metabolic_spiking$ python fig3_nw.py

This will output Figure3_nw.png file

Figure S4 network model supplementary plot

   (py36)user@computer:~/metabolic_spiking$ python nw_va.py vogels2005
   (py36)user@computer:~/metabolic_spiking$ python nw_va.py metabolic

Each of the above command takes a long time to execute (>12Hrs depending on your machine), and can take up ~10Gb space on your machine. It creates the data for the summary plots from the original paper, and for this paper for one seed and with connectivity of 0.02 are reported. Please note that both seed and connectivity were also tested and the results are nearly identical (not shown). This is easily achivied in the code). Only after these two are executed the following command will work. Many files are created (./netsim_results/20/*_spks.pkl) with actual spike times and other useful variables.

After this, we compute the avalanche properties for the datasets from above. For this use the following for bulk summary analysis.

 	(py36)user@computer:~/metabolic_spiking$ python avalan_props.py vogels2005
(py36)user@computer:~/metabolic_spiking$ python avalan_props.py metabolic

These commands will produce as many files with the summary data as pickles (./netsim_results/20/*_summary.pkl)

 	(py36)user@computer:~/metabolic_spiking$ python fig3_nw_supp.py

When the above is run for the first time, the summary values are calculated and dumped into a .npz file, which is used for the subsequent figure generation calls. In the end, you will have Figure3_nw_supp.png file in your folder.

Other files

• figure_properties.py Contains the defaults for figure creation
• utils.py Some useful functions for Recording data during simulations, Q definition is also here
• channel.py Class definition of an ion channel
• gates.py Class definitions of ion channel gates and definitions of ROS gates used in PD/MiniSOG/AOX
• mitochondria.py Class definition of the Nazaret mitochondrial model which is used in this work
• nazaret_mito.py Original nazaret model converted to python - this file as such is not used in this work
• avalan_props.py Function definitions used to compute avalanche properties
• lifcell.py Class def of a hybrid leaky integrate and fire neuron model
• mitosfns.py Helper functions that are used in the figure generation.

Contact

Please reach out to me, by creating an issue on the github, if you find any bugs or if you would like to contribute/extend/improve this work.

License

Copyright 2021 Chaitanya Chintaluri

This software is provided under the GNU General Purpose License version 3.0, You will find a copy of this license within this folder, or from online here: https://www.gnu.org/licenses/gpl-3.0.txt