microRNA_noise

Here deposites the code for simulating gene expression noise modulated by microRNA as well as calculating gene expression noise from flow cytometry data.

Simulate gene expression noise modulated by microRNA

This code is created and tested with MATLAB 2020b.

Run simulation/solve_noise.m to simulate noise, and run simulation/plot_noise.m to plot the result. An example of the result with default parameters is shown in simulation/sample_output.

Set the type of model

There are three types of preset miRNA regulation models, which can be set by the parameter type in Line 7 of simulation/plot_noise.m. For competing RNAs, type = 1; for repetitive targets of same miRNAs, type = 2; for multiple targets of different miRNAs, type = 3.

Set parameters

All parameters involved in the model should be set before simulation in the set fixed parameters section. All parameters that should be altered for simulating different conditions should be set in the set altered parameters section.

The simulation step size and range should be assigned in the set simulation parameters section. The simulation range determines the range that the gene expression is simulated in by setting the production rate of the target gene’s mRNA (kT). We recommend users to set a wide range with a large step size to quickly determine the lower and upper limit of the range, and then narrow the step size to gain a refined simulation result.

Calculate gene expression noise from flow cytometry data

This code is created and tested with R 3.6.1. ggplot2 is necessary for visualizing the result.

Run flow_cytometry/plot.r to calculate noise from the sample data in flow_cytometry/data. Sample outputs are shown in flow_cytometry/result.

Contact

For any issues, please contact weilei92@tsinghua.edu.cn.