Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017)

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Accession:187615
Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012).
Reference:
1 . Mäki-Marttunen T, Lines GT, Edwards AG, Tveito A, Dale AM, Einevoll GT, Andreassen OA (2017) Pleiotropic effects of schizophrenia-associated genetic variants in neuron firing and cardiac pacemaking revealed by computational modeling Translational Psychiatry 7:5
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic cell; Cardiac atrial cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; Na/Ca exchanger; I_SERCA; Na/K pump; Kir;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C;
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Python;
Model Concept(s): Schizophrenia;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; Na/Ca exchanger; I_SERCA; Na/K pump; Kir;
Scripts for simulating the neuron and cardiac cell models and producing Figure 1 and 2.
(CC BY 3.0) Tuomo Maki-Marttunen 2016

To run the scripts, run command 'runme' in MATLAB. Note that you may have to run the
NEURON simulations from command line instead of running them through MATLAB. This can
be done by runnning 'sh runme.sh' in folders hay/ and almog/.

Folders included:

hay/: Scripts and files for running the Hay model (Hay E, Hill S, Schuermann F, Markram H, and Segev I (2011): Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties, PLoS Comput Biol 7: e1002107)
-Most of the files based on the implementation given in https://senselab.med.yale.edu/modeldb/showModel.cshtml?model=139653
-Files specific to this entry:
       hay/calcifcurves.py: Python script with NEURON interface to calculate the f-I curves
       hay/calcsteadystates.py: Python script with NEURON interface to calculate the membrane potential time courses for steady-state firing
       hay/findDCshortthreshold.py: Python script with NEURON interface to find the smallest DC amplitudes for inducing a spike
       hay/mutation_stuff.py: Table of mutations
       hay/mytools.py: Generic tools for e.g. determining spike times
       hay/collectfig1.py: Script for refining the results for Figure 1
       hay/collectfig2.py: Script for refining the results for Figure 2
       hay/runme.sh: Shell script for running the Hay-model simulation part from runme.m
-Pre-calculated data:
       hay/fig1_curves.mat: MATLAB data file for plotting the Hay model results for Figure 1 (only the results that are heaviest to simulate are included)
       hay/fig2_curves.mat: MATLAB data file for plotting the Hay model results for Figure 2 (only the results that are heaviest to simulate are included)
       
almog/: Scripts and files for running the Almog model (Almog M and Korngreen A (2014): A quantitative description of dendritic conductances and its application to dendritic excitation in layer 5 pyramidal neurons, J Neurosci 34.1: 182-196)
-Most of the files based on the implementation given in https://senselab.med.yale.edu/modeldb/showModel.cshtml?model=151825
-Files specific to this entry:
       almog/calcifcurves.py: Python script with NEURON interface to calculate the f-I curves
       almog/calcsteadystates.py: Python script with NEURON interface to calculate the membrane potential time courses for steady-state firing
       almog/findDCshortthreshold.py: Python script with NEURON interface to find the smallest DC amplitudes for inducing a spike
       almog/mutation_stuff.py: Table of mutations
       almog/mytools.py: Generic tools for e.g. determining spike times
       almog/collectfig1.py: Script for refining the results for Figure 1
       almog/collectfig2.py: Script for refining the results for Figure 2
       almog/runme.sh: Shell script for running the Almog-model simulation part from runme.m
-Pre-calculated data:
       almog/fig1_curves.mat: MATLAB data file for plotting the Almog model results for Figure 1 (only the results that are heaviest to simulate are included)
       almog/fig2_curves.mat: MATLAB data file for plotting the Almog model results for Figure 2 (only the results that are heaviest to simulate are included)

kharche/: Scripts and files for running the Kharche model (Kharche S, Yu J, Lei M, and Zhang H (2011): A mathematical model of action potentials of mouse sinoatrial node cells with molecular bases, Am J Physiol-Heart Circ Physiol 301.3: H945-H963)
-Model implementation based on the previous entry (C-code) https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=141274
-Order of certain variable updates changed in kharche_SA.m w.r.t. the original code in order to give a state update that is consistent across different dt
       calcrates_kharche.m: MATLAB script for simulating the cells implemented with variants
       getDefVals_kharche.m: MATLAB function that returns the default model parameters
       getMT_kharche.m: MATLAB function that returns the table of mutations
       kharche_SA.m: MATLAB function that simulates the SANC model and returns the membrane potential time courses and other data
       runcontrol_kharche.m: MATLAB script for running the control SANC model

severi/: Scripts and files for running the Severi model (Severi S, Fantini M, Charawi LA, and DiFrancesco D (2012): An updated computational model of rabbit sinoatrial action potential to investigate the mechanisms of heart rate modulation, J Physiol 590.18: 4483-4499)
-Model implementation based on the CellML entry https://models.physiomeproject.org/w/sseveri/severi_fantini_charawi_difrancesco_2012
       calcrates_severi.m: MATLAB script for simulating the cells implemented with variants
       getDefVals_severi.m: MATLAB function that returns the default model parameters
       getMT_severi.m: MATLAB function that returns the table of mutations
       severi_SA.m: MATLAB function that simulates the SANC model and returns the membrane potential time courses and other data
       runcontrol_severi.m: MATLAB script for running the control SANC model


Files in the main folder:

runme.m: Script for running all required simulations to draw Figure 1 and 2
drawfig1: Script for drawing Figure 1
drawfig2: Script for drawing Figure 2
getMT_kharche.m, getgenenames.m: Auxiliary MATLAB files for the mutation data
interpolate.m, interpolate_multidim.m, membpotderivs.m, subplottight2.m: Auxiliary MATLAB tools


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