Neural-network chemical master equation (NN-CME)

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This repository contains the julia code and parameters corresponding to the effeciency test presented in: Neural network aided approximation and parameter inference of stochastic models of gene expression

File descriptions:

• "SSA_single_channel_tau10_.csv" is the source data
• "main.ipynb" is the code for training and testing NN-CME in Jupyter notebook
• "NN-CME.png" shows the prediction result by means of NN-CME
• "processed.mat" summarizes the experimental data from literature for Fig. 4b (inset)

Requirements:

• Julia >= 1.4.2
• Flux v0.10.4
• DifferentialEquations v6.15.0
• DiffEqSensitivity v6.26.0

How to run:

1. Install Jupyter notebook by conda/pip, or you can use Anaconda

   conda install jupyter notebook

   pip install jupyter notebook

2. Add 'IJulia' in the julia console Pkg.add('IJulia')

3. cd to the resository in terminal (Linux) or command window (Windows) and open jupyter notebook web jupyter notebook

The method is well described in:

• Q. Jiang et. al. Neural network aided approximation and parameter inference of stochastic models of gene expression. bioRxiv (2020).