A computational approach/model to explore NMDA receptors functions (Keller et al 2017)

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Accession:235002
"... Here, we describe a general computational method aiming at developing kinetic Markov-chain based models of NMDARs subtypes capable of reproducing various experimental results. These models are then used to make predictions on additional (non-obvious) properties and on their role in synaptic function under various physiological and pharmacological conditions. For the purpose of this book chapter, we will focus on the method used to develop a NMDAR model that includes pharmacological site of action of different compounds. Notably, this elementary model can subsequently be included in a neuron model (not described in detail here) to explore the impact of their differential distribution on synaptic functions."
Reference:
1 . Keller AF, Bouteiller JC, Berger TW (2017) Development of a Computational Approach/Model to Explore NMDA Receptors Functions. Methods Mol Biol 1677:291-306 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment:
Model Concept(s): Ion Channel Kinetics;
Implementer(s):
Search NeuronDB for information about:  NMDA;
/
KellerEtAl2017
readme.txt
KELLER_Book_Chapter_figure3_NMDA_fct_Mg.py
NMDA8_v6_2L3.xml
                            
This is the readme for the model associated with the book chapter:

Development of a Computational Approach/Model to Explore NMDA Receptors Functions (2017)

These files model the response of an NMDA receptor kinetic model to a 1ms pulse of glutamate (8 microM) as a function of magnesium concentration.

Content of the zip file: 3 files including this readme.

One file is the NMDA model (XML file). 
The other is a python script used to run the simulation and plot the results. The results plotted correspond to figure 3 of the paper.

Requirements:  Libroadrunner (free open source simulation engine), Python, Numpy and MatplotLib.

To run:  
In a terminal, type:    python KELLER_Book_Chapter_figure3_NMDA_fct_Mg.py

Result corresponding to Fig. 3 of the book chapter will appear in the folder 
after successful run (name: NMDA-R Current (pA)_all_Mg_concentrations.pdf)

For additional details, email jbouteil_at_usc.edu


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