noiseRPA

Overview

This repository contains all MATLAB codes used to generate the figures in the manuscript, "Toward Single-Cell Control: Noise-Robust Perfect Adaptation in Biomolecular Systems". The codes are written in MATLAB R2024a. Pre-simulated results, named 'noiseRPA.zip', can be found in realease v1.0.

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Folder Structure and Description

Figure 1

• Fig1.m

  • Reproduces panels Fig. 1b, c, e, f, and g.
  • By default, it plots the green lines in Fig. 1e, f.
  • Setting mu2 = theta2 = eta2 = d2 = 0 produces the blue lines in Fig. 1c, d.
  • Setting all of mu1, theta1, eta1, k1, mu2, theta2, eta2, d2 = 0 and p1 = 4.5 produces the gray lines in Fig. 1c, d, e, f.

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Figure 2

• Fig2a.m

  • Reproduces Fig. 2a.

• Fig2b.m

  • Generates the data for the plot in Fig. 2b.
  • Running the script produces a CSV file (e.g., Fig2b_fano.csv) with four rows, each corresponding to a different lines in Fig. 2b.

• Fig2c.m

  • Produces the heatmap for Fig. 2c.
  • Requires the presence of a file named stochastic_simulation_1000 in the same directory.
  • Running this script saves a heat matrix file named Fig2c_heatmap_ff.mat.

• Fig2c_drawing.m

  • Visualizes the heatmap matrices generated by the Fig2c.m scripts.

• Pre-simulated Result Folders

  • Fig2c_mn: Results for blue lines in Fig. 2c

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Figure 3

• Fig3a_simulation.m

  • Simulates data for the heatmap in Fig. 3a.
  • Requires the file stochastic_simulation_1000 in the same directory.
  • Generates 9 files named heatmap_ff_eta1_{eta1}_eta2_{eta2}.mat, saved in the Fig3a subfolder.

• Fig3b_simulation.m

  • Simulates data for the heatmap in Fig. 3b.
  • Requires the file stochastic_simulation_1000.
  • Generates 9 files named heatmap_ff_k1_{k1}_d2_{d2}.mat, saved in the Fig3b subfolder.

• Fig3c_simulation.m

  • Simulates data for the heatmap in Fig. 3c.
  • Requires the file stochastic_simulation_on2.
  • Saves the matrix as Fig3c_heatmap_ff.mat.

• Fig3d_simulation.m

  • Simulates data for the heatmap in Fig. 3d.
  • Requires the file stochastic_simulation_on3.
  • Saves the matrix as Fig3d_heatmap_ff.mat.

• Fig3_drawing.m

  • Visualizes the heatmap matrices generated by the _simulation.m scripts.

• Pre-simulated Result Folders

  • Fig3a_mn: Results for blue lines in Fig. 3a
  • Fig3b_mn: Results for blue lines in Fig. 3b
  • Fig3c_mn: Results for blue lines in Fig. 3c
  • Fig3d_mn: Results for blue lines in Fig. 3d

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Figure 4

• Fig4_telegraph.m

  • Simulates and draws plots in Fig. 4b-c.
  • Generates 3 files named telegraph.mat, telegraph_MC.mat, telegraph_MC_NC.mat.

• Fig4_toggle_switch.m

  • Simulates and draws plots in Fig. 4e-f.
  • Generates 3 files named toggle_switch.mat, toggle_switch_MC.mat, toggle_switch_MC_NC.mat.

• Pre-simulated Result Folders

  • telegraph: Simulation results of the telegraph model used in Fig. 4b-c
  • telegraph_MC: Simulation results of the telegraph model with MC used in Fig. 4b-c
  • telegraph_MC_NC: Simulation results of the telegraph model with MC and NC used in Fig. 4b-c
  • toggle_switch: Simulation results of the toggle_switch model used in Fig. 4e-f
  • toggle_switch_MC: Simulation results of the toggle_switch model with MC used in Fig. 4e-f
  • toggle_switch_MC_NC: Simulation results of the toggle_switch model with MC and NC used in Fig. 4e-f

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Figure 5

• Fig5bc.m

  • Plots the histograms shown in Fig. 5b, c.

• Fig5ef.m

  • Plots the histograms shown in Fig. 5e, f.

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Figure S1

• FigS1.m

  • Simulates and draws plots in Fig. S1b.

• Pre-simulated Result File

  • FigS1_converged_NC.csv: Converged value for the initial condition of simulation

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Figure S2

• FigS2.m

  • Simulates and draws plots in Fig. S2.

• Pre-simulated Result Files

  • FigS2_noAW_mean.mat: Simulated mean trajectory without anti-windup in Fig. S2a, b
  • FigS2_noAW_variance.mat: Simulated variance trajectory without anti-windup in Fig. S2a, b
  • FigS2_AW_mean.mat: Simulated mean trajectory with anti-windup in Fig. S2c, d
  • FigS2_AW_variance.mat: Simulated variance trajectory with anti-windup in Fig. S2c, d

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Figure S3

• FigS3.m

  • Simulates and draws plots in Fig. S3.

• Pre-simulated Result Files

  • FigS3_mu2_8.7_mean.mat: Simulated mean trajectory where mu = 8.7
  • FigS3_mu2_8.7_sc.mat: Simulated single-cell trajectory where mu = 8.7
  • FigS3_mu2_9.3_mean.mat: Simulated mean trajectory where mu = 9.3
  • FigS3_mu2_9.3_sc.mat: Simulated single-cell trajectory where mu = 9.3
  • FigS3_mu2_9.9_mean.mat: Simulated mean trajectory where mu = 9.9
  • FigS3_mu2_9.9_sc.mat: Simulated single-cell trajectory where mu = 9.9
  • FigS3_mu2_10.5_mean.mat: Simulated mean trajectory where mu = 10.5
  • FigS3_mu2_10.5_sc.mat: Simulated single-cell trajectory where mu = 10.5

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Figure S4

• FigS4.m

  • Simulates and draws plots in Fig. S4.

• Pre-simulated Result Files

  • FigS4_per_20_mean.mat: Simulated mean trajectory with 20-fold perturbation
  • FigS4_per_20_variance.mat: Simulated variance trajectory with 20-fold perturbation
  • FigS4_per_50_mean.mat: Simulated mean trajectory with 50-fold perturbation
  • FigS4_per_50_variance.mat: Simulated variance trajectory with 50-fold perturbation
  • FigS4_per_100_mean.mat: Simulated mean trajectory with 100-fold perturbation
  • FigS4_per_100_variance.mat: Simulated variance trajectory with 100-fold perturbation
  • FigS4_per_10000_mean.mat: Simulated mean trajectory with 10000-fold perturbation
  • FigS4_per_10000_variance.mat: Simulated variance trajectory with 10000-fold perturbation

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Figure S6

• FigS6b_NC.m

  • Simulates and draws plots of the Fano factor trajectory of the network with MC and NC in Fig. S6b.

• FigS6b_origin.m

  • Simulates and draws plots of the Fano factor trajectory of the network with aPI controller in Fig. S6b.

• FigS6d_NC.m

  • Simulates and draws plots of the Fano factor trajectory of the network with MC and NC in Fig. S6d.

• FigS6d_origin.m

  • Simulates and draws plots of the Fano factor trajectory of the network with negative action in Fig. S6d.

• FigS6f_NC.m

  • Simulates and draws plots of the Fano factor trajectory of the network with MC and NC in Fig. S6f.

• FigS6f_origin.m

  • Simulates and draws plots of the Fano factor trajectory of the network with sensor-actuated AIF in Fig. S6f.

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Figure S7

• FigS7b.m

  • Simulates and draws plots of the Fano factor trajectory in Fig. S7.

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Requirements

• Servers

  • Servers are required for faster simulation
  • Fig2b.m, Fig2c.m, Fig3a_simulation.m, Fig3b_simulation.m, Fig3c_simulation.m, Fig3d_simulation.m, FigS2.m, FigS3.m, FigS4.m, FigS7.m, and all files in the Figure S6 folder were simulated on an Intel Xeon Gold 6348 CPU.

• MATLAB R2024a

  • Install time for MATLAB differs by the versions and computing devices, but it typically takes less than 1 hour

• The following simulation files must be present in the relevant folders:

  • stochastic_simulation_1000
  • stochastic_simulation_on2
  • stochastic_simulation_on3

• The estimated simulation time: ("normal" computer here means Intel Core i7-6700 CPU):

  • Fig1.m: (with normal computer) < 10 minutes for original network and original network with MC; 1 ~ 2 hours for original network with MC and NC
  • Fig2a.m: (with normal computer) less than 20 minutes
  • Fig2b.m: (with server) 2 hours
  • Fig2c.m: (with server) 7 hours
  • Fig3a_simulation.m: (with server) 3-4 days
  • Fig3b_simulation.m: (with server) 3-4 days
  • Fig3c_simulation.m: (with server) 7 hours
  • Fig3d_simulation.m: (with server) 7 hours
  • Fig4_telegraph.m: (with server) 5 hours
  • Fig4_toggle_switch.m: (with server) 5 hours
  • FigS1.m: (with normal computer) less than 10 minutes
  • FigS2.m: (with server) 7 hours
  • FigS3.m: (with server) 2 hours
  • FigS4.m: (with server) 1-2 days
  • Files in Figure S6: (with server) 3 hours for files that end with '_origin', 1 day for files that end with '_NC'
  • FigS7.m: (with server) 1 hour