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ZuckermanLab/Bayesian_Transporter_Paper

 
 

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Supporting code for Bayesian Transporter research - still under development.

Getting started

  • create an environment with required packages/dependencies using conda env create -f environment.yml
  • run through the example_notebook.ipynb notebook for example usage and analysis
    • ensure that the filepaths are correctly set for your system in the configuration file ...config.yaml configuration file.
  • an \example directory is included in the repo which contains data from a short pocomc sampling run, along with an example configuration file, SBML and tellurium transporter model, and synthetic observed dataset.

Stand alone usage

  • For Bayesian inference using EMCEE run python YOUR_PATH_HERE/run_emcee.py in a terminal
  • For Bayesian inference using PocoMC run python YOUR_PATH_HERE/run_pocomc.py in a terminal
  • For Maximum Likelihood Estimation using Scipy Optimization run python YOUR_PATH_HERE/run_optimizer.py.py in a terminal

Ensure that the configuration file and file path is updated for each module as needed

Limitations / issues

Docs

More detailed API documentation is provided below:

August George, 2023

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Transporter mechanism identification using Bayesian inference

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  • Jupyter Notebook 87.6%
  • Python 12.4%