How to use
First you have to start TIde with the launcher from Applications/System Tools. (Alternatively you can hit
Alt-F2 and then type xterm -e "cd /usr/lib/python2.5/site-packages/TIde && sudo bash".)
For the analysis of a single file you have to call python model.py -f path_to_model in the TIde main directory including the options
| option | meaning | example | optional/mandatory | default |
|---|
| -a | Observable, e.g. species_1 which is accumulating in the pathological state | species_1 | mandatory |
| -s | Model is a signalling pathway | | optional |
| -i | Different effective inhibitor concentrations | 0.1,1,10 | optional | 0.1 |
| -g | Use only certain reactions as possible modification targets | reaction_0,reaction_1 | optional | all |
| -l | Use up to this many modifications simultaneously | 2 | optional | 1 |
| -t | Do the computation by a simulated titration | | optional | |
| -e | Estimate effective inhibitor concentrations to achieve a reduction of the aim value to this factor. | 0.1 | optional | |
After all computations have been performed you can call different output methods, e.g. html.py,
htmllist.py, or pdfplot.py.
The first argument to the tools is the name of the output file which has to be placed in the
folder named after the model which was created in the previous step. E.g. python html.py models/simple_signalling_model/sdh
will create the file models/simple_signalling_model/sdh.html.
The options for html.py are
| option | meaning | example | optional/mandatory | default |
|---|
| -i | Ignore lines containing the strings or lacking the strings, e.g. show only combinations including reaction_3 but ignore entries with the effective
inhibitor concentration of 0.375. | ^reaction_3,0.375 | optional |
| -s | Ignore control/response coefficients in the output. | | optional |
while the options for pdfplot.py are
| option | meaning | example | optional/mandatory | default |
|---|
| -r | Range of the x axis | 1,100 | optional |
| -x | Take only curves whichs name matches this regular expression | ".*(1|5).*n$" | optional | .* |
Another interesting script for analysing data of a 2-dimensional scan for good inhibition targets is sdhanalysis.py.
With its help synergisms and antagonisms in the inhibition results can be revealed. The command line options for this script are
| option | meaning | example | optional/mandatory | default |
|---|
| -t | Threshold how much stronger a synergism has to be compared to the sum of single effects | 5 | optional | 1 |
| -v | Verbosity | 1 | optional | 0 |
|