LegacyVersion1.2.0

Framework for Integration of Neuronal Data and SIgnaling Models.

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.

We have moved our Experiment datasheet from TSV to Json file format and Python 2 is no longer supported by the Python Software Foundation, moose-core which is our primary backend simulation tool will also stop supporting Python2 compilation. we have moved on to Python3.

This version of the code is tested against python2 and python3 with moose-core,

BhallaLab/moose-core @ 2062ab2 version (tag Version FindSim-1.0-beta) with python2 and

BhallaLab/moose-core @ 0ee20b3cd9ff9 with python3.

Update

• Added SBML version of model along with genesis model

• Added capability for FindSim to work with

  • BarChart, Vclamp, python files
  • Synaptic plasticity protocols: when EPSP/EPSC type readouts it triggers synaptic input and measures slope and peak of resulting EPSP/EPSC
  • handle direct parameter assignments
  • handle the -o flag which does optimization for mixed elec-chem models by skipping quiet periods in the elec stimulus.
  • option provided to scale Kd and tau for reacs
  • generate a file of parameters that can be tweaked in specified subset of model
  • handle multiple scaled parameters
  • handle presettling
  • framework for handling fEPSP, with better implementation using an integral function to approximate many stimulated cells in a thin layer in a slice
  • Fixes script so that we can correctly handle model subsetting even in multiscale models.
  • update to findSim to seperate out MOOSE class from main FindSim script,enable other simulators.

• Added experiments of

  • BarChart, Vclamp, directParamsDemo
  • added a directory TestTSV with examples of many of the kinds of experiment protocols which includes fEPSP.

• Capability to do parameter Sweeps:

  • The capability to run on all tsv files in the specified directory with modification of the selected parameters, computes the mean score for each run and present the "best"(parameter,score), the result can be output to file. This can run in parallel using python's multiprocessing library from runAllParallel.
  • added feature to use either directory (existing) or file (new) to specify which experiment files to use. The file option has a list of experiment files followed by weights to assign to each.

• Added runAllParallel: Script to run files in parallel using python's multiprocessing library.

• Added Brent minimization for 1 param, and multi_param_minimization: Script does a one-dimensional minimization on the model. It runs the findSim program on all tsv files in the specified directory with modifications of the selected parameters. It computes the weighted score for each run as the return value for the minimization function. While the Brent algorithm is serial, there are lots of indvidual tsv calculations for each step that can be done in parallel.

• Update to Brent to handle presetting

• print out more details about the scores for each experiment.

• added to tweak model file in accordance with optimization results

Bug fix:
- used Qentry in heapq to make ordering unambiguous
- Clean up of string handling in tsv files,eliminate issues due to dangling carriage return
- fix made in findsim script for handling multiscale models, loadhh.py and to handle long-standing issue of non-match of dumped optimized file.

See Bug report here

Third release

With addition scripts one can do basic parameter sweep on the model. It runs the findSim program on all tsv files in the specified directory with modifications of the selected parameters. It computes the mean score for each run and presents the "best" (parameter, score) along with next-best flanking. It can do this in parallel using Python's multiprocessing library.

FindSim_Paper_Release

Framework for Integration of Neuronal Data and SIgnaling Models.

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.

FindSim scripts for the paper were run with BhallaLab/moose-core@12cf83e version or FindSim-1.0-alpha with python2, libsbml L3V.

The same was run BhallaLab/moose-core@2062ab2 version or tag version FindSim-1.0-beta with python2 and found the result matched as documented in the paper.

This is for paper submission

This is for the paper submission