Signaling pathways underlying LTP in hippocampal CA1 pyramidal cells (Jedrzejewska-Szmek et al 2017)

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" ...We investigated whether the diverse experimental evidence can be unified by creating a spatial, mechanistic model of multiple signaling pathways in hippocampal CA1 neurons. Our results show that the combination of activity of several key kinases can predict the occurrence of long-lasting forms of LTP for multiple experimental protocols. ..."
1 . J?drzejewska-Szmek J, Luczak V, Abel T, Blackwell KT (2017) ß-adrenergic signaling broadly contributes to LTP induction. PLoS Comput Biol 13:e1005657 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Gap Junctions:
Transmitter(s): Ephinephrine;
Simulation Environment: NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity; Signaling pathways;
Implementer(s): Blackwell, Avrama [avrama at]; Jedrzejewska-Szmek, Joanna ;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Ephinephrine;
All the model files are in model_files directory. This directory has 4
subdirectories: long_dend, which contains model files for spatial
simulations of a 20 um long dendrite; model_70_percent, model_100_percent and model_120_percent
contain files for simulations of a short dendrite. model_100_percent is the basal model, model_70_percent and
model_120_percent were used for robustness evaluation.  All the scripts used in
analysis and to generate the figures are in directory scripts.

To successfully run a model, you need the master model file, which
specifies reaction file, initial conditions file, morphology file,
stimulation file and output config file.

Run the simulation using version 2.1.10 of NeuroRD.
This version of the java program is called stochdiff2.1.10.jar and is located in scripts/.
To run NeuroRD:
java -jar stochdiff2.1.10.jar <model_file>

NeuroRD generates two files: one
ending with '-mesh.txt', which specifies morphology, the other ending
with '-conc.txt' containing amounts of molecular species (specified in
output config file) in the region specified in output config file.

Names of master model files
start with
for the short dendrite simulations and
"Model_long_dendrite_PKAc_times_3_switching_L_pump_neurogranin_" for
the long dendrite simulations. Initial conditions file names start
with "IC_switching_steady_L_pump_neurogranin", reaction file names
start with
stimulation files start with "Stim_" and morphology files start with
"morph". If the master file (or any other file) was used for
simulations of bath application of bAR agonists and antagonists, or
blocked PKA conditions, it is also indicated in the file
name. Probably the safest thing is to have all the model files (of a
particular model) in one directory.

An alternative method of running the simulations is to use the python script:

python <model_master_file> <length of the simulation> [other options] will make a new master_file with the ending runtime_[length of the simulation].xml

If you run without specifing the runtime, the script will run the simulations for one simulation step and generate a new master model file <master model file>_runtime_<simulation step>.xml and a new output file <master model file>_whole_output.xml.
The output config specifies providing output for every molecule in the simulation.
It will generate a new output config file (using initial conditions file)
unless one specifies not to (e.g. for the long dendrite simulations).  

This python script is very convenient for simulating the short dendrite,
however, for simulations of the longer denrite, you might want to use
a custom output config file (only getting output of a subset of the molecules) 
to make the simulations a bit faster and concentration files smaller.

gives all the options of (also how
to specify custom output file).

Additional information on
If you use this python script to run your simulations, you will have to change the neurord path
inside the code or specify it in command line using --path. If you use
it without --no_run switch, the model will either run for a specified
time (in ms) or for dt and add runtime_{specified time} to the models
filename. I run my models with --segment_concentrations and
--segment_list=PSD,head,neck to get concentrations in the spine and
dendrite. You can also to generate
concentrations while model is still running.
One can use the same python script to perform just the analysis with a --no_run switch.
--chosen_species will prevent generating the output config file and instead use the custom one specified in the master model file.

I use to look at concentrations of individual molecular species.

To generate Tables S1 and S2 use and respectively. Both scripts also contain
information about the path of data files used to generate
tables. Please update them to match your directory tree.

Running multispine models I also use Here I specify a custom output file
(Long_dend_output.xml) because the data files are huge and difficult
to analyze. I use --segment_concentrations and then use to obtain specie concentrations in the spines. For
species used in dendritic signature I use I generated
figures using averaged traces (obtained with I use to get duration above the threshold for
individual traces. To generate figure 10 use (with
averaged traces). and are the main files governing figures, specifying which
file names to use to plot data. You will have to update those paths to
match your directory tree. contains all the functions I used.
Fig 3 was generated using
Fig 4 was generated using
Fig 5 was generated using
Figs 6&7 were generated using
Figs 8&9 were generated using
Fig 10 was generated using
Fig 11 was generated using To extract data from conc
files I used and
Fig 11 was generated using, model files are in fig_12 directory

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