Framework for Integration of Neuronal Data and SIgnaling Models. The associated paper is available at https://doi.org/10.3389/fninf.2018.00038

The FindSim project maps models of neural and cellular signaling to experimental protocols and readouts. It runs the experiment on the model, and provides a score that reports how closely the two match.

This file and the files in this repository are licensed under GPL v3 or later.

One can get FindSim using python-pip.

To install last stable release (NOTE This uses moose version 3.1.4 (outdated with this release))

 $ pip install FindSim 

Or, to install the nightly built,

 $ pip install FinSim --pre   # recommended 

Alternatively, you can download the source code and install it yourself.

1. Install MOOSE which can be found here https://moose.ncbs.res.in/readthedocs/install/install.html
2. Install FindSim

git clone https://github.com/BhallaLab/FindSim
cd FindSim
python setup.py install  

After successful installation, two commands are available at your disposal findsim and findsim_parallel. To see the help message, pass -h option to either of these commands. For example, findsim -h will show you following message.

usage: findsim [-h] [-m MODEL] [-d DUMP_SUBSET] [-p PARAM_FILE] [-t] [-hp]
               [-hs] [-o] [-s SCALE_PARAM SCALE_PARAM SCALE_PARAM]
               [-settle_time SETTLE_TIME]
               script

FindSim argument parser This program loads a kinetic model, and runs it with
the specified stimuli. The output is then compared with expected output
specified in the same file, to generate a model score.

positional arguments:
  script                Required: filename of experiment spec, in tsv format.

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Optional: model filename, .g or .xml
  -d DUMP_SUBSET, --dump_subset DUMP_SUBSET
                        Optional: dump selected subset of model into named
                        file
  -p PARAM_FILE, --param_file PARAM_FILE
                        Optional: Generate file of tweakable params belonging
                        to selected subset of model
  -t, --tabulate_output
                        Flag: Print table of plot values. Default is NOT to
                        print table
  -hp, --hide_plot      Hide plot output of simulation along with expected
                        values. Default is to show plot.
  -hs, --hide_subplots  Hide subplot output of simulation. By default the
                        graphs include dotted lines to indicate individual
                        quantities (e.g., states of a molecule) that are being
                        summed to give a total response. This flag turns off
                        just those dotted lines, while leaving the main plot
                        intact.
  -o, --optimize_elec   Optimize electrical computation. By default the
                        electrical computation runs for the entire duration of
                        the simulation. With this flag the system turns off
                        the electrical engine except during the times when
                        electrical stimuli are being given. This can be *much*
                        faster.
  -s SCALE_PARAM SCALE_PARAM SCALE_PARAM, --scale_param SCALE_PARAM SCALE_PARAM SCALE_PARAM
                        Scale specified object.field by ratio.
  -settle_time SETTLE_TIME, --settle_time SETTLE_TIME
                        Run model for specified settle time and return dict of
                        {path,conc}.
						

Command findsim_parellel is a helper utility: it runs multiple simulations using findsim in parellel.

To run one of the example experiments on the default model (synSynth7.g) and generate a graph to compare model to experiment.

findsim Curated/FindSim-Jain2009-Fig2B.tsv  

You can also pass the model explicitly,

findsim Curated/FindSim-Jain2009-Fig2B.tsv --model models/synSynth7.g  

To findSim program on all tsv files in the specified directory, computes their scores, and prints out basic stats of the scores, you should use findsim_parallel command.

findsim_parellel Curated -n 8 

It will run the entire set of Curated experiments in parallel using 8 independant processes (it will be effectively if your computer has at least 8 cores). The value of -n should not be more than N+1 where N is the number of processors on your system (use system utility nproc to see this number).

More detailed invocation of this command looks like the following:

findsim_parallel path/to/tsv/files -n 6 --model synSynth7.g

FindSim/             		: project directory  
FindSim/stable			: Stable branch. Stable version of `develop` branch  
FindSim/Curated	   		: Folder contains FindSim worksheets to which the model is well fit.
FindSim/models			: Model files 
FindSim/findSim.py			: Main findSim script  
FindSim/runAllParallel.py		: Batch/parallel wrapper script.  
FindSim/FindSim-Exptworksheet.xlsx	: Template worksheet with inline help and units, for Microsoft Excel.  
FindSim/FindSim-Exptworksheet.ods	: Template worksheet with inline help and units, for Libre Office.  
FindSim/FindSim-Schema.xml 		: Schema for tsv files for worksheet.  
FindSim/README.md			: This file  

Generating Figures for FindSim: a Framework for Integrating Neuronal Data and Signaling Models by Nisha A. Viswan, G.V. HarshaRani, Melanie I. Stefan, Upinder S. Bhalla Front Neuroinform. 2018 Jun 26;12:38. doi: 10.3389/fninf.2018.00038. eCollection 2018.

All these are run using the branch release.1.1.0. Please change into the branch to run these.

Figure 6: bottom
python findSim.py Curated/FindSim-Jain2009_Fig4F.tsv --model models/synSynth7.g

Figure 7B:
python findSim.py Curated/FindSim-Bhalla1999_fig2B.tsv --model models/synSynth7.g
python findSim.py Curated/FindSim-Gu2004_Fig3.tsv --model models/synSynth7.g

Figure 7C:
python findSim.py Curated/FindSim-Ji2010_fig1C_ERK_acute.tsv --model models/synSynth7.g

Figure 7D:
python findSim.py Curated/FindSim-Bhalla1999_fig4C.tsv --model models/synSynth7.g

• The web template for experiment worksheet can be found here https://www.ncbs.res.in/faculty/bhalla-findsim/worksheet

• The MOOSE site: http://moose.ncbs.res.in. MOOSE documentation: http://moose.ncbs.res.in/readthedocs/install/index_install.html

• Two papers were used as the initial basis for the models, and which in turn refer to a large number of experimental studies for their data:

  1. Bhalla US., Iyengar R. Emergent properties of networks of biological signaling pathways. Science. 1999 Jan 15;283(5400):381-7.
  2. Jain P, and Bhalla, U.S. Signaling logic of activity-triggered dendritic protein synthesis: an mTOR gate but not a feedback switch. PLoS Comput Biol. 2009 Feb;5(2):e1000287. Epub 2009 Feb 13

• Two more papers were used for some of the experiments

  1. Gu J, et al. Beta1,4-N-Acetylglucosaminyltransferase III down-regulates neurite outgrowth induced by costimulation of epidermal growth factor and integrins through the Ras/ERK signaling pathway in PC12 cells. Glycobiology. 2004 Feb;14(2):177-86. Epub 2003 Oct 23
  2. Ji Y, et al. Acute and gradual increases in BDNF concentration elicit distinct signaling and functions in neurons. Nat Neurosci. 2010 Mar;13(3):302-9. doi: 10.1038/nn.2505. Epub 2010 Feb 21.

• DOQCS database, from which models were derived: http://doqcs.ncbs.res.in Sivakumaran S. et al. The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks. Bioinformatics. 2003. 19(3):408–415