This repository provides the code to do the simulations we have in the paper (Biophysical clocks face a trade-off between internal and external noise resistance). Folders are divided according to the figures that the code generates.
To use the kaiABC.py, try
python kaiABC.py [ATP_min] [ATP_max] [eta] [sfactor] [stoch_flag] [env_noise]
For example,
python kaiABC.py 0.2 0.8 1 1 0 0
The code will print out the MI value. The details of the values we use in the figure 1 can be found in the Appendix 1.
Files beginning with 'plot' will run their corresponding file beginning with 'simulate' multiple times to generate a plot similar to Figure 2 in the paper. For example, try running plot_MI_vs_intnoise_millar.m in MATLAB. This will run simulate_millar.m with different values of internal noise and parameters (which can be found in the Appendix 2 of the paper).
Similar to Fig2 folder, the files beginning with 'plot' will run their corresponding file beginning with 'simulate' multiple times to generate the plots. For example, plot_MI_vs_days.m will run simulate_MI_vs_day.m multiple times to generate the subfigure in figure 6.
First run the Matlab code “run_this.m” to generate the raw datas. Then the first step of making the figure is done by the Matlab code “priliminary_generation_of_plot.m”. With the results from the Matlab code, save the figure 1 as “priliminary_c.fig”, and save figure 2 as “priliminary_e.fig”. Then run the Matlab code “Final_generation_of_figure_5_c_and_e.m” to collect data from these plots and generate Figure 5 panel e and c. The final plots will be saved under the name “c.pdf” and “e.pdf”
First, run the Matlab code “run_this.m” to generate the raw data. Then generate the plot by running the Matlab code “code_for_plotting.m” which gives the plot for Figure 5 panel d. The plot can be saved into pdf format and the axis can be scaled as the reader wishes.