State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)

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Accession:149174
A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.
Reference:
1 . Thomas EA, Petrou S (2013) Network-specific mechanisms may explain the paradoxical effects of carbamazepine and phenytoin. Epilepsia 54:1195-202 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Axon; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Dentate gyrus granule GLU cell; Dentate gyrus mossy cell; Dentate gyrus basket cell; Dentate gyrus hilar cell;
Channel(s): I Na,t; I A; I_AHP;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Ion Channel Kinetics; Epilepsy; Calcium dynamics; Drug binding; Markov-type model;
Implementer(s): Thomas, Evan [evan at evan-thomas.net];
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; I Na,t; I A; I_AHP; Gaba; Glutamate;
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ThomasPetrou2013
Fig 2A
Fig 4
README
                            
This is the readme for the model associated with the paper:

Thomas EA, Petrou S (2013) Network-specific mechanisms may explain the
paradoxical effects of carbamazepine and phenytoin. Epilepsia

These are the source files to generate Fig 2A (left panel) and the
data for Fig 4.

1) The first is Matlab code and demonstrates how the Markov model is
build from a Hodgkin Huxely model. Run Vclamp.m

2) The second is the network model. Do the usual NEURON thing to build
the mechanism executable. Edit runall.py to work with your environment
then run 'python ./runall.py' to submit the jobs that generate the
data.

Any questions? Email me evan@evan-thomas.net.