GitHub - ModelDBRepository/222715: Model of a BDNF feedback loop (Zhang et al 2016)

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
BDNFloop-model-Zhang-2016.ode		BDNFloop-model-Zhang-2016.ode
readme.html		readme.html
screenshot.png		screenshot.png
simple_plot.m		simple_plot.m

Repository files navigation

<html><pre>
This is the readme for the XPP-Auto code associated with the paper:

Zhang Y, Smolen P, Alberini CM, Baxter DA, Byrne JH (2016)
Computational model of a positive BDNF feedback loop in hippocampal
neurons following inhibitory avoidance training. Learn Mem 23:714-722

This ode model was contributed by Yili Zhang.

This model requires XPP to be installed which is freely available from
<a href="http://www.math.pitt.edu/~bard/xpp/xpp.html">http://www.math.pitt.edu/~bard/xpp/xpp.html</a>

Usage:

Download and extract this archive. Run the included ode file, for
example on unix/linux type on the command line:

xppaut BDNFloop-model-Zhang-2016.ode -silent

After ten minutes or so a test.txt data file will be created which
corresponds to the control values used in Figure 1 and 2.
A simple matlab program is provided that graphs the output columns:

<img src="./screenshot.png" alt="screenshot" width="550">

---

Parameter sensitivity analysis (takes hours to run):

The setting '@RANGE=1, RANGEOVER=step, RANGESTEP=1100, RANGELOW=0,
RANGEHIGH=1100, RANGERESET=yes, RANGEOLDIC=yes, output=test1' is only
used to run parameter sensitivity analysis. You can get 1,100 data
files if you remove the comment on this line.  Re-running with this
generates files named test1.0, test1.1, test1.2, ...

In each of the data files, the value of one parameter is varied
between -90% and 90%.  If you only need the control case, using my
current setting '@ output=test.txt' is enough.
</pre></html>