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make_figures.m
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make_figures.m
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%% Make all figures for Forero 2020
%
% Usage:
% paper_fig_num | choose one: {'3a','3d','3f','sup7','sup10','sup8a','sup8b'}
% save_eps | 1/0 true/false bool for saving the figures
%
% figure 3a: Data vs Model Autocorrelations and Cross correlations for all 3 signals +
% dwell time bootstraping + MLE/uncertainty of model
%
% figure 3d: Intensity histograms data vs model for CTD and Ser5 and mRNA
% count comparisons
%
% figure 3f: Sample trace from simulation of chosen model
%
% Sup figure 7: MetHaste data visualization, 40,000 points
%
% Sup figure 8a: RNAP / mRNA busrting trace
%
% Sup figure 8b: Simulated chips with different states of the model
% WARNING THIS TAKES AT LEAST 10 GB of RAM
%
% Sup figure 10: Perturbation analysis of the model (inhib approximation)
function make_figures(paper_fig_num, save_eps)
figure_names = {'3a','3d','3f','sup7','sup10','sup8a','sup8b'};
if ~any(strcmp(figure_names,paper_fig_num))
disp('not a valid figure number, use one of the following')
disp(figure_names)
return
end
%Final figure code that generates all computational model figures used for
%Forero2020
%Set up figure sizes
X_SIZE = 13; Y_SIZE = 15;
figure(1);clf;
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, X_SIZE, Y_SIZE]; % x,y, width, height
global_color = 'k';
fntsize = 18;
xh = 2*3*6;
yh = 2*14.3/3;
subplot(3,1,1)
norm_intensity = 0;
minYlim = -0.1;
maxYlim = 5;
yyaxis left
%add the local paths
addpath('./Data_files')
addpath('./Model_files')
addpath('./Parameter_files')
addpath('./Functions')
addpath('./Model_fits/simple_MH_results/3.17.2021')
rng(0)
%% Load in data from excel files and normalize them
%Load Cross Correlations with G0 normalization
[~,~,~,mrna_rnap,mrna_ser5,ser5_rnap] = load_normalization_variance_norm_cc(0); %load normalization 21 pts for cross correlations
%Load Autocorrelations with G0_intp normalization and no rezeroing
[mrna,ser5,rnap,~,~,~] = load_normalization_variance(1,'G0_intp','none',10);
load('best_simple_pars.mat','parameters') %load parameter file
%% Generate analytical correlation signals
[sigred,TT,means,sig,TT2,mins] = get_ac_and_cc_mod_simplified(parameters,[0:.1:30]);
nchains = 20;
nsegments = 50;
close all
figure(1)
SPS =[];
mh_val_all = [];
if strcmp(paper_fig_num,'3a')
%% make figure 3a, cross and autocorrelations
% load in the MCMC samples for the gray fill
for i=1:nchains
%try
mh_smpl{i} = [];
mh_value{i} = [];
sv_file = ['./Model_fits/simple_MH_results/3.17.2021/met_hast_pars_splitE_',num2str(i)];
clear mh_smpl_* mh_value_*
tmp_dat = load(sv_file);
for j=1:nsegments
eval(['mh_smpl{i} = [mh_smpl{i};tmp_dat.mh_smpl_',num2str(j),'];']);
eval(['mh_value{i} = [mh_value{i};tmp_dat.mh_value_',num2str(j),'];']);
end
SPS = [SPS;mh_smpl{i}];
mh_val_all = [mh_val_all; mh_value{i}];
end
mh_pars = SPS;
lags1 = [0:31];
%% RNAP AUTOCORRELATION
fntsize = 18; %set up figure 3
close all
figure(1)
subplot(4,3,2)
fig1= gcf;
%set(gcf,'color','k');
fig1.PaperUnits = 'centimeters';
xh = 2*3*6;
yh = 2*14.3/3;
fig1.PaperPosition = [0, 0, 2/3*xh, 2*yh]; % x,y, width, height
%sem_rnap = std(Xdata_sem(:,1:30),1)/(sqrt(20));
plot(TT, sigred(1,:),'k-','linewidth',2) %plot model rnap_acc
hold on;
%plot the data rnap_acc
b1=errorbar(lags1(1:25), rnap.mn_ac(1:25), rnap.sem_ac(1:25),'o','MarkerSize',5,'MarkerFaceColor',[256, 0, 0]./256,'Color',[256, 0, 0]./256);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
minvec = sigred(1,:);
maxvec = sigred(1,:); %randomly sample 1000 mh par lines to generate gray lines
rng(0)
for i=1:1000 % Sample MCMC for gray fill
n = ceil(rand*size(mh_pars,1));
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2_samp,mins_samp] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
for j = 1:length(minvec) %keep the minimum and maximum points
if sigred_samp(1,j) > maxvec(1,j)
maxvec(1,j) = sigred_samp(1,j);
end
if sigred_samp(1,j) < minvec(1,j)
minvec(1,j) = sigred_samp(1,j);
end
end
end
%fill in the points between min and max MH sampled signals with gray
fill([TT',fliplr(TT')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]);
b1=errorbar(lags1(1:25), rnap.mn_ac(1:25), rnap.sem_ac(1:25),'o','MarkerSize',5,'MarkerFaceColor',[256, 0, 0]./256,'Color',[256, 0, 0]./256);hold on;
plot(TT, sigred(1,:),'k-','linewidth',2) %replot so model acc signal is on top
% Setup labels and limits
xlim([-.5,25])
plot([0,25],[0,0],'k--')
ylim([-.1,1.3])
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color','Ycolor',global_color);
title({'\color{black}Autocorrelations'},'FontSize',fntsize,'FontWeight','bold')
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
title({'CTD'},'FontSize',fntsize,'FontWeight','bold')
%% SER5ph ACC FIGURE
subplot(4,3,5) %SER5 ACC figure
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 3*yh]; % x,y, width, height
rnap_color = [138, 25, 38]./256;
ser5_color = [20, 122, 47]./256;
plot(TT,sigred(5,:),'-k','linewidth',2) %plot model ser5 acc
hold on;
% plot data ser5 acc
b1=errorbar(lags1(1:25), ser5.mn_ac(1:25), ser5.sem_ac(1:25),'s','MarkerSize',5,'MarkerFaceColor',[0, 256, 0]./256,'Color',[0, 256, 0]./256);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
pt = 5;
minvec = sigred(pt,:);
maxvec = sigred(pt,:);
rng(0)
for i=1:1000 %sample and get min / max from 1000 random mcmc signals
n = ceil(rand*size(mh_pars,1));
parameters_samp(par_changed) = par_samp;
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2_samp,mins_samp] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
%plot(TT_samp, sigred_samp(5,:),'-','Color',[.8,.8,.8],'linewidth',2)
for j = 1:length(sigred_samp) %store the minimum and maximum points for grayfill
if sigred_samp(pt,j) > maxvec(1,j)
maxvec(1,j) = sigred_samp(pt,j);
end
if sigred_samp(pt,j) < minvec(1,j)
minvec(1,j) = sigred_samp(pt,j);
end
end
end
%fill in the gray area and replot other signals on top
fill([TT',fliplr(TT')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]);
b1=errorbar(lags1(1:25), ser5.mn_ac(1:25), ser5.sem_ac(1:25),'s','MarkerSize',5,'MarkerFaceColor',[0, 256, 0]./256,'Color',[0, 256, 0]./256);hold on;
plot(TT,sigred(5,:),'-k','linewidth',2)
xlim([-.5,25]) %figure labels and limits
plot([0,25],[0,0],'k--')
ylim([-.1,1.3])
title({'Ser5ph'},'FontSize',fntsize,'FontWeight','bold')
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
%% MRNA ACC FIGURE
subplot(4,3,8) %mRNA ACC figure
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 3*yh]; % x,y, width, height
rnap_color = [138, 25, 38]./256;
ser5_color = [20, 122, 47]./256;
mrna_color = [22, 68, 148]./256;
plot(TT,sigred(9,:),'-k','linewidth',2) %model mrna ACC
hold on;
%data mrna ACC
b1=errorbar(lags1(1:25), mrna.mn_ac(1:25), mrna.sem_ac(1:25),'d','MarkerSize',5,'MarkerFaceColor',[0, 0, 256]./256,'Color',[0, 0, 256]./256);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
pt = 9;
minvec = sigred(pt,:);
maxvec = sigred(pt,:);
rng(0)
for i=1:1000 %get mh min and max for gray fill
n = ceil(rand*size(mh_pars,1));
parameters_samp(par_changed) = par_samp;
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2_samp,mins_samp] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
%plot(TT_samp, sigred_samp(9,:),'-','Color',[.8,.8,.8],'linewidth',2)
for j = 1:length(sigred_samp)
if sigred_samp(pt,j) > maxvec(1,j)
maxvec(1,j) = sigred_samp(pt,j);
end
if sigred_samp(pt,j) < minvec(1,j)
minvec(1,j) = sigred_samp(pt,j);
end
end
end
%fill the min max mh pars, and replot signals on top
fill([TT',fliplr(TT')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]);
b1=errorbar(lags1(1:25), mrna.mn_ac(1:25), mrna.sem_ac(1:25),'d','MarkerSize',5,'MarkerFaceColor',[0, 0, 256]./256,'Color',[0, 0, 256]./256);hold on;
plot(TT,sigred(9,:),'-k','linewidth',2)
xlim([-.5,25]) %Limits and labels
plot([0,25],[0,0],'k--')
ylim([-.1,1.3])
title({'mRNA'},'FontSize',fntsize,'FontWeight','bold')
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
if save_eps
saveas(gca,'./Figures/Accs.epsc') %save the autocorrelations
return
%% SER5PH RNAP CC FIGURE
lags2 = [-10:10]; %time vector for CCs
subplot(4,3,3)
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 3*yh]; % x,y, width, height
% Ser5 RNAP cross correlation
plot([-TT(end:-1:2);TT],[sigred(4,end:-1:2),sigred(2,:)],'k-','linewidth',2) %model
hold on;
%data
b1=errorbar(lags2, ser5_rnap.mn_cc, ser5_rnap.sem_cc,'s','MarkerSize',5,'MarkerFaceColor',[0, 0.67, 1],'Color',[0, 0.67, 1]);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
minvec = [sigred(4,end:-1:2),sigred(2,:)]; %pull the mh min max for fill of gray
maxvec = [sigred(4,end:-1:2),sigred(2,:)];
rng(0)
for i=1:1000 %sample the MCMC for gray fill
n = ceil(rand*size(mh_pars,1));
parameters_samp(par_changed) = par_samp;
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2_samp,mins_samp] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
%plot([-TT_samp(end:-1:2);TT_samp],[sigred_samp(4,end:-1:2),sigred_samp(2,:)],'-','Color',[.8,.8,.8],'linewidth',2)
svec = [sigred_samp(4,end:-1:2),sigred_samp(2,:)];
for j = 1:length(svec)
if svec(1,j) > maxvec(1,j)
maxvec(1,j) = svec(1,j);
end
if svec(1,j) < minvec(1,j)
minvec(1,j) = svec(1,j);
end
end
end
fill([[-TT(end:-1:2);TT]',fliplr([-TT(end:-1:2);TT]')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]); %fill in gray
b1=errorbar(lags2, ser5_rnap.mn_cc, ser5_rnap.sem_cc,'s','MarkerSize',5,'MarkerFaceColor',[0, 0.67, 1],'Color',[0, 0.67, 1]);hold on;
plot([-TT(end:-1:2);TT],[sigred(4,end:-1:2),sigred(2,:)],'k-','linewidth',2) %replot signals
plot([0,0],[0,1.3],'--','Color',global_color)
%plotting labels and legends and limits
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
title({'\color{black}Cross-correlations'},'FontSize',20,'FontWeight','bold')
title({'Ser5ph-CTD'},'FontSize',fntsize,'FontWeight','bold')
xlim([-10,10])
ylim([-.1,1.3])
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
%% MRNA RNAP CC FIGURE
subplot(4,3,6)
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 3*yh]; % x,y, width, height
plot([-TT(end:-1:2);TT],[sigred(7,end:-1:2),sigred(3,:)],'k-','linewidth',2) % mrna_rnap CC model
hold on; %mrna_rnap CC data
b1=errorbar(lags2, mrna_rnap.mn_cc, mrna_rnap.sem_cc,'o','MarkerSize',5,'MarkerFaceColor',[1, .5, 0],'Color',[1, .5, 0]);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
minvec = [sigred(7,end:-1:2),sigred(3,:)];
maxvec = [sigred(7,end:-1:2),sigred(3,:)];
rng(0)
for i=1:1000 %load min and max mh_pars for gray bars
n = ceil(rand*size(mh_pars,1));
parameters_samp(par_changed) = par_samp;
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2,mins] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
% plot([-TT_samp(end:-1:2);TT_samp],[sigred_samp(7,end:-1:2),sigred_samp(3,:)],'-','Color',[.8,.8,.8],'linewidth',2)
svec = [sigred_samp(7,end:-1:2),sigred_samp(3,:)];
for j = 1:length(svec)
if svec(1,j) > maxvec(1,j)
maxvec(1,j) = svec(1,j);
end
if svec(1,j) < minvec(1,j)
minvec(1,j) = svec(1,j);
end
end
end
fill([[-TT(end:-1:2);TT]',fliplr([-TT(end:-1:2);TT]')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]);
b1=errorbar(lags2, mrna_rnap.mn_cc, mrna_rnap.sem_cc,'o','MarkerSize',5,'MarkerFaceColor',[1, .5, 0],'Color',[1, .5, 0]);hold on;
plot([-TT(end:-1:2);TT],[sigred(7,end:-1:2),sigred(3,:)],'k-','linewidth',2)
plot([0,0],[0,1.3],'--','Color',global_color)
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
title({'mRNA-CTD'},'FontSize',fntsize,'FontWeight','bold')
xlim([-10,10])
ylim([-.1,1.3])
%% mRNA SER5PH CC
subplot(4,3,9) %mrna_ser5 signal
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 3*yh]; % x,y, width, height
plot([-TT(end:-1:2);TT],[sigred(8,end:-1:2),sigred(6,:)],'k-','linewidth',2)
hold on;
b1=errorbar(lags2, mrna_ser5.mn_cc, mrna_ser5.sem_cc,'d','MarkerSize',5,'MarkerFaceColor',[.5, 0, 1],'Color',[.5, 0, 1]);hold on;
par_fixed = parameters;
par_changed = [1,3:6];
parameters_samp = parameters;
minvec = [sigred(8,end:-1:2),sigred(6,:)];
maxvec = [sigred(8,end:-1:2),sigred(6,:)];
rng(0)
for i=1:1000
n = ceil(rand*size(mh_pars,1));
parameters_samp(par_changed) = par_samp;
par_samp = mh_pars(n,:);
parameters_samp(par_changed) = par_samp;
[sigred_samp,TT_samp,means_samp,sig_samp,TT2_samp,mins_samp] = get_ac_and_cc_mod_simplified(parameters_samp,[0:.1:30]);
%plot([-TT_samp(end:-1:2);TT_samp],[sigred_samp(8,end:-1:2),sigred_samp(6,:)],'-','Color',[.8,.8,.8],'linewidth',2)
svec = [sigred_samp(8,end:-1:2),sigred_samp(6,:)];
for j = 1:length(svec)
if svec(1,j) > maxvec(1,j)
maxvec(1,j) = svec(1,j);
end
if svec(1,j) < minvec(1,j)
minvec(1,j) = svec(1,j);
end
end
end
fill([[-TT(end:-1:2);TT]',fliplr([-TT(end:-1:2);TT]')], [minvec, fliplr(maxvec)],[.8,.8,.8], 'EdgeColor', [.8,.8,.8]);
b1=errorbar(lags2, mrna_ser5.mn_cc, mrna_ser5.sem_cc,'d','MarkerSize',5,'MarkerFaceColor',[.5, 0, 1],'Color',[.5, 0, 1]);hold on;
plot([-TT(end:-1:2);TT],[sigred(8,end:-1:2),sigred(6,:)],'k-','linewidth',2)
plot([0,0],[0,1.3],'--','Color',global_color)
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set (gca ,'TickLength',[.01,.3],'LineWidth',1);
title({'mRNA-Ser5ph'},'FontSize',fntsize,'FontWeight','bold')
xlim([-10,10])
ylim([-.1,1.3])
if save_eps
saveas(gca,'./Figures/ccs_nodist.epsc')
end
%% Acov taus, load in the bootstrapped dwell time calculations for speed
% and reproducability
T_array = [0:1:1000];
mrna_bootstrapped_tau = [];
ser5_bootstrapped_tau = [];
rnap_bootstrapped_tau = [];
mrna_sim_acov = zeros(400,32);
ser5_sim_acov = zeros(400,32);
rnap_sim_acov = zeros(400,32);
k = 0;
%% Code to boot strap taus if needed
% while k < 400
% t = [0:1:199];
%
%
% simulated_pol2 = zeros(20,200);
% simulated_ser5 = zeros(20,200);
% simulated_mrna = zeros(20,200);
%
% for i = 1:20
% sol = run_single_SSA(x0,S,W,T_array,time_var,signal_update_rate);
% [pol2_ssa,ser5_ssa,ts_ssa,~] = get_model_intesities(sol,0,0,0); %convert molecules to signal
% [pol2norm,ser5norm,tsnorm] = Normalize_simulated_intensities(.95,pol2_ssa,ser5_ssa,ts_ssa);
% simulated_pol2(i,:) = pol2norm(end-199:end);
% simulated_ser5(i,:) = ser5norm(end-199:end);
% simulated_mrna(i,:) = tsnorm(end-199:end);
% end
%
% [mrna_sim,ser5_sim,rnap_sim,~,~,~,G1_rnap_sim,G1_ser5_sim,G1_mrna_sim] = get_simulated_cc([0:1:199],simulated_pol2', simulated_ser5', simulated_mrna',0);
%
%
% decorr_mrna = t(mrna_sim.mn_ac < .01);
% if length(decorr_mrna) == 0
% continue
% end
%
%
% decorr_ser5 = t(ser5_sim.mn_ac < .01);
%
% if length(decorr_ser5) == 0
% continue
% end
% decorr_rnap = t(rnap_sim.mn_ac < .01);
%
% if length(decorr_rnap) == 0
% continue
% end
%
% k = k + 1
% mrna_bootstrapped_tau = [mrna_bootstrapped_tau, decorr_mrna(1)];
% ser5_bootstrapped_tau = [ser5_bootstrapped_tau, decorr_ser5(1)];
% rnap_bootstrapped_tau = [rnap_bootstrapped_tau, decorr_rnap(1)];
%
% mrna_sim_acov(k,:) = mrna_sim.mn_ac;
% ser5_sim_acov(k,:) = ser5_sim.mn_ac;
% rnap_sim_acov(k,:) = rnap_sim.mn_ac;
%
% end
%%
cd taus
load('rnap_sim_acov_nonoise.mat')
load('ser5_sim_acov_nonoise.mat')
load('mrna_sim_acov_nonoise.mat')
cd ..
%%
thresh = .2; %count the dwell time as when the autocorrelation crosses 20% decorrelation
t = [0:1:199];
mrna_bootstrapped_tau = [];
ser5_bootstrapped_tau = [];
rnap_bootstrapped_tau = [];
for i = 1:400 %get the decorrelation times
tmpacov = t(mrna_sim_acov(i,:) < thresh);
mrna_bootstrapped_tau = [mrna_bootstrapped_tau, tmpacov(1)];
tmpacov = t(ser5_sim_acov(i,:) < thresh);
ser5_bootstrapped_tau = [ser5_bootstrapped_tau, tmpacov(1)];
tmpacov = t(rnap_sim_acov(i,:) < thresh);
rnap_bootstrapped_tau = [rnap_bootstrapped_tau, tmpacov(1)];
end
% figure(32)
% histogram(mrna_bootstrapped_tau,'FaceColor','b')
% hold on;
% histogram(ser5_bootstrapped_tau,'FaceColor','g')
% histogram(rnap_bootstrapped_tau,'FaceColor','r')
%%
figure(1)
% subplot(4,3,2)
% plot( [mean(rnap_bootstrapped_tau),mean(rnap_bootstrapped_tau)], [-1,6],'r', 'LineWidth',2)
% x1 = mean(rnap_bootstrapped_tau)-std(rnap_bootstrapped_tau);
% x2 = mean(rnap_bootstrapped_tau)+std(rnap_bootstrapped_tau);
% fill( [x1,x2, x2,x1], [-1,-1,6,6],'r', 'FaceAlpha',.2,'LineStyle','none')
%
% subplot(4,3,5)
% plot( [mean(ser5_bootstrapped_tau),mean(ser5_bootstrapped_tau)], [-1,6],'g', 'LineWidth',2)
% x1 = mean(ser5_bootstrapped_tau)-std(ser5_bootstrapped_tau);
% x2 = mean(ser5_bootstrapped_tau)+std(ser5_bootstrapped_tau);
% fill( [x1,x2, x2,x1], [-1,-1,6,6],'g', 'FaceAlpha',.2,'LineStyle','none')
%
% subplot(4,3,8)
% plot( [mean(mrna_bootstrapped_tau),mean(mrna_bootstrapped_tau)], [-1,6],'b', 'LineWidth',2)
%
%
% x1 = mean(mrna_bootstrapped_tau)-std(mrna_bootstrapped_tau);
% x2 = mean(mrna_bootstrapped_tau)+std(mrna_bootstrapped_tau);
% fill( [x1,x2, x2,x1], [-1,-1,6,6],'b', 'FaceAlpha',.2,'LineStyle','none')
%
%
cd taus
load('rnap_sim_acov.mat')
load('ser5_sim_acov.mat')
load('mrna_sim_acov.mat')
cd ..
t = [0:1:199];
mrna_bootstrapped_tau = [];
ser5_bootstrapped_tau = [];
rnap_bootstrapped_tau = [];
for i = 1:400
tmpacov = t(mrna_sim_acov(i,:) < thresh);
mrna_bootstrapped_tau = [mrna_bootstrapped_tau, tmpacov(1)];
tmpacov = t(ser5_sim_acov(i,:) < thresh);
ser5_bootstrapped_tau = [ser5_bootstrapped_tau, tmpacov(1)];
tmpacov = t(rnap_sim_acov(i,:) < thresh);
rnap_bootstrapped_tau = [rnap_bootstrapped_tau, tmpacov(1)];
end
% add the dwell time lines on the autocorrelations
figure(1)
subplot(4,3,2)
plot( [mean(rnap_bootstrapped_tau),mean(rnap_bootstrapped_tau)], [-1,6],'r', 'LineWidth',2); hold on;
x1 = mean(rnap_bootstrapped_tau)-std(rnap_bootstrapped_tau);
x2 = mean(rnap_bootstrapped_tau)+std(rnap_bootstrapped_tau);
fill( [x1,x2, x2,x1], [-1,-1,6,6],'r', 'FaceAlpha',.2,'LineStyle','--','edgecolor','r')
xlim([-.5,25])
ylim([-.1,1.3])
subplot(4,3,5)
plot( [mean(ser5_bootstrapped_tau),mean(ser5_bootstrapped_tau)], [-1,6],'g', 'LineWidth',2); hold on;
x1 = mean(ser5_bootstrapped_tau)-std(ser5_bootstrapped_tau);
x2 = mean(ser5_bootstrapped_tau)+std(ser5_bootstrapped_tau);
fill( [x1,x2, x2,x1], [-1,-1,6,6],'g', 'FaceAlpha',.2,'LineStyle','--','edgecolor','g')
xlim([-.5,25])
ylim([-.1,1.3])
subplot(4,3,8)
plot( [mean(mrna_bootstrapped_tau),mean(mrna_bootstrapped_tau)], [-1,6],'b', 'LineWidth',2); hold on;
x1 = mean(mrna_bootstrapped_tau)-std(mrna_bootstrapped_tau);
x2 = mean(mrna_bootstrapped_tau)+std(mrna_bootstrapped_tau);
fill( [x1,x2, x2,x1], [-1,-1,6,6],'b', 'FaceAlpha',.2,'LineStyle','--','edgecolor','b')
xlim([-.5,25])
ylim([-.1,1.3])
if save_eps
saveas(gca, './Figures/corrs_with_dwell.epsc') %save the whole figure
end
return
end
end
if strcmp(paper_fig_num,'3f')
%% SSA trajectory for bottom of figure 3
figure(1)
% Load in parameters
kon = parameters(1);
koff = 1000;%parameters(2);
kesc = parameters(3);
kproc = parameters(4);
kin = 1000*parameters(5);
kout = parameters(6);
frac = parameters(7);
eta_rnap = parameters(8);
eta_ser5 = parameters(9);
eta_ts = parameters(10);
Nstates = 3; %set up propensity and stoich matrixes to run the gillespie
b = zeros(Nstates,1);
b(1) = kon;
c = zeros(3,3);
c(1,1:3)=[0,1,1];
c(2,1:3)=[0,frac,1]; %signal matrix
c(3,3)=1;
S = zeros(3,6);
W1 = zeros(6,3);
W0 = zeros(6,1);
S(1,1) = 1; W1(1,1) = -kon; W0(1,1) = kon; %fill in the rates
S(1,2) = -1; W1(2,1) = koff;
S(2,3) = 1; W1(3,1) = kin;
S(2,4) = -1; W1(4,2) = kout;
S(2:3,5) = [-1;1]; W1(5,2) = kesc*frac;
S(3,6) = -1; W1(6,3) = kproc;
x0 = [0,0,0]'; %inital condition
T_array = [0:1:1000]; %time vector
time_var = 0;
signal_update_rate = 0;
W = @(x) W1*x + W0; %propensity function
rng(45)
%Solve a single trajectory with seed 45
sol = run_single_SSA(x0,S,W,T_array,time_var,signal_update_rate);
pol2_ssa = sol(2,:)';
ser5_ssa = sol(2,:)';
ts_ssa = sol(3,:)';
[pol2_ssa,ser5_ssa,ts_ssa,~] = get_model_intesities(sol,eta_rnap,eta_ser5,eta_ts); %convert molecules to signal
[pol2norm,ser5norm,tsnorm] = Normalize_simulated_intensities(.95,pol2_ssa,ser5_ssa,ts_ssa); %normalize the simulated signals
X_SIZE = 13; Y_SIZE = 15;
figure(1);clf;
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, X_SIZE, Y_SIZE]; % x,y, width, height
global_color = 'k';
cla;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, 1.1*yh]; % x,y, width, height
avpol2 =movmean(pol2norm(end-200:end),3); %moving average the trajectories
avser5 = movmean(ser5norm(end-200:end),3);
avts = movmean(tsnorm(end-200:end),3);
plot(avpol2(1:201),'r','linewidth',2); hold on; plot(avser5(1:201),'g','linewidth',2); plot(avts(1:201),'b','linewidth',2 )
legend('POL2','SER5ph','mRNA')
title('Representative trace','FontSize',fntsize,'FontWeight','bold')
ylabel('Normalized intensity','FontSize',fntsize,'FontWeight','bold')
xlabel('Time (min)','FontSize',fntsize,'FontWeight','bold')
xlim([0,200])
ylim([-.5,1.5])
if save_eps
saveas(gca, './Figures/trace.epsc') %save the whole figure
end
return
end
%% Supplemental Figure
% Plot the molecule signals with bursting highlighted and filled in
if strcmp(paper_fig_num,'sup8a')
%rename the parameters
kon = parameters(1);
koff = 1000;%parameters(2);
kesc = parameters(3);
kproc = parameters(4);
kin = 1000*parameters(5);
kout = parameters(6);
frac = parameters(7);
eta_rnap = parameters(8);
eta_ser5 = parameters(9);
eta_ts = parameters(10);
Nstates = 3; %set up propensity and stoich
b = zeros(Nstates,1);
b(1) = kon;
c = zeros(3,3);
c(1,1:3)=[0,1,1];
c(2,1:3)=[0,frac,1]; %signal matrix
c(3,3)=1;
S = zeros(3,6);
W1 = zeros(6,3);
W0 = zeros(6,1);
S(1,1) = 1; W1(1,1) = -kon; W0(1,1) = kon; %fill in the S and W1 matrix
S(1,2) = -1; W1(2,1) = koff;
S(2,3) = 1; W1(3,1) = kin;
S(2,4) = -1; W1(4,2) = kout;
S(2:3,5) = [-1;1]; W1(5,2) = kesc*frac;
S(3,6) = -1; W1(6,3) = kproc;
x0 = [0,0,0]';
T_array = [0:1:1000]; %time vector
time_var = 0;
signal_update_rate = 0;
W = @(x) W1*x + W0; %propensity function
figure(1)
rng(1095) %set the seed
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh, yh]; % x,y, width, height
T_array = [0:.1:200];
sol = run_single_SSA(x0,S,W,T_array,time_var,signal_update_rate);
pol2_ssa = (sol(2,:) + sol(3,:))'; %pol2 is in all channels
ser5_ssa = (sol(2,:)+ sol(3,:))';
ts_ssa = sol(3,:)'; %mRNA is only in state 3
bubble_top = pol2_ssa(end-500:end); %get top and bottom for red fill
bubble_bottom = ts_ssa(end-500:end);
p2 = pol2_ssa(end-500:end)-ts_ssa(end-500:end);
on = (p2 > 5);
%fill between the top and bottom
fill([T_array(1:501),fliplr(T_array(1:501))], [(on*500)'-500, fliplr((on*500)')],[220,220,220]./256,'linestyle','None','HandleVisibility','off' )
hold on;
fill([T_array(1:501),fliplr(T_array(1:501))], [bubble_top', fliplr(bubble_bottom')], [255, 212, 212]./256, 'linestyle','None')
% SSA
plot(T_array(1:501),pol2_ssa(end-500:end),'r','linewidth',2); hold on; plot(T_array(1:501),ts_ssa(end-500:end),'b','linewidth',2 )
ylim([0,150])
xlabel({'Time (min)'},'FontSize',fntsize,'FontWeight','bold')
ylabel({'Simulated molecule counts'},'FontSize',fntsize,'FontWeight','bold')
a = legend({'Burst','CTD','mRNA'},'FontSize',fntsize,'FontWeight','bold');
%set (gca ,'TickLength',[.01,.3],'LineWidth',2);
set(a, 'Box', 'off');
if save_eps
saveas(gca,'./Figures/Burst_traj.epsc')
end
return
end
%%
if strcmp(paper_fig_num, '3d')
%Figure 3 histograms from simulations
rng(0)
kon = parameters(1);
koff = 1000;%parameters(2);
kesc = parameters(3);
kproc = parameters(4);
kin = 1000*parameters(5);
kout = parameters(6);
frac = parameters(7);
eta_rnap = parameters(8);
eta_ser5 = parameters(9);
eta_ts = parameters(10);
Nstates = 3; %set up propensity and stoich
b = zeros(Nstates,1);
b(1) = kon;
c = zeros(3,3);
c(1,1:3)=[0,1,1]; %signal matrix
c(2,1:3)=[0,frac,1];
c(3,3)=1;
%setup propensity and stoich matrices
S = zeros(3,6);
W1 = zeros(6,3);
W0 = zeros(6,1);
S(1,1) = 1; W1(1,1) = -kon; W0(1,1) = kon;
S(1,2) = -1; W1(2,1) = koff;
S(2,3) = 1; W1(3,1) = kin;
S(2,4) = -1; W1(4,2) = kout;
S(2:3,5) = [-1;1]; W1(5,2) = kesc*frac;
S(3,6) = -1; W1(6,3) = kproc;
time_var = 0;
signal_update_rate = 0;
W = @(x) W1*x + W0; %propensity function
x0 = [0,0,0]'; %inital condition
data = xlsread('Nasc_TranscriptsCount.xlsx'); %load in mRNA data
mrna_hist = data(:,1);
mrna_hist = mrna_hist(1:end-5);
%sample 1000 trajectories to generate histograms from
pol2_ssa_traj = [];
ser5_ssa_traj = [];
ts_ssa_traj = [];
mrnai_traj = [];
rnap_c = [];
ser5_c = [];
mrna_c = [];
for i = 1:1000
sol = run_single_SSA(x0,S,W,[0:1:2000],time_var,signal_update_rate); % solve the SSA
pol2_ssa = sol(2,:)'; %these are unused
ser5_ssa = sol(2,:)';
ts_ssa = sol(3,:)'; %get just the mRNA traj
mrnai = ts_ssa;
[pol2_ssa,ser5_ssa,ts_ssa,~] = get_model_intesities(sol,eta_rnap,eta_ser5,eta_ts); %generate the pol2_ssa/ser5_ssa
[pol2norm,ser5norm,tsnorm] = Normalize_simulated_intensities(.95,pol2_ssa,ser5_ssa,ts_ssa);
pol2_ssa_traj = [pol2_ssa_traj, pol2norm(1:200:end)]; %append the signals
ser5_ssa_traj = [ser5_ssa_traj, ser5norm(1:200:end)];
ts_ssa_traj = [ts_ssa_traj, ts_ssa(1:200:end)];
mrnai_traj = [mrnai_traj, mrnai(1:200:end)];
end
pol2_traj = pol2_ssa_traj'; %transpose to plot
ser5_traj = ser5_ssa_traj';
ts_traj = ts_ssa_traj';
[rnap_I, ser5_I, mrna_I] = Normalize_raw_intensities(.95); %get the normalized intensities from the data 95 percentile
rnap_data = reshape(rnap_I(1:20:end,:),1,200); %rnap_data sorted by 20 time incrementes (decorrelated) this was for histograms
ser5_data = reshape(ser5_I(1:20:end,:),1,200);
mrna_data = reshape(mrna_I(1:20:end,:),1,200);
fntsize = 18;
%% Plot the CTD histograms
figure(11)
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0,xh/3, 3*yh/3]; % x,y, width, height
[x,n] = hist(reshape(pol2_traj.',1,[]),30);
[11, 252, 3]./256;
[218, 51, 255]./256;
x1 = histogram(reshape(pol2_traj.',1,[]),n,'Normalization','probability','FaceColor',[11, 252, 3]./256,'FaceAlpha',.2,'linewidth',.01,'edgecolor',[11, 252, 3]./256)%,'DisplayStyle','stairs')
hold on;
x2 = histogram(reshape(pol2_traj.',1,[]),n,'Normalization','probability','FaceColor','none','FaceAlpha',.2,'linewidth',3,'edgecolor',[11, 252, 3]./256,'DisplayStyle','stairs')
x3 = histogram(rnap_data ,n,'Normalization','probability','FaceColor',[218, 51, 255]./256,'FaceAlpha',.2,'linewidth',.01,'edgecolor',[218, 51, 255]./256)%,'DisplayStyle','stairs')
x4 = histogram(rnap_data ,n,'Normalization','probability','FaceColor','none','FaceAlpha',.2,'linewidth',3,'edgecolor',[218, 51, 255]./256,'DisplayStyle','stairs')
a = legend(gca, [x1,x3],{'Model','Data'},'Location','Best')
set(a, 'Box', 'off');
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set(a,'TextColor',global_color);
ylim([0,.15])
xlim([-1.3,1.5])
set(gca,'linewidth',2)
if save_eps
saveas(gca,'./Figures/pol2_hist.epsc')
end
set(gca,'YTickLabel',[],'XtickLabel',[])
if save_eps
saveas(gca,'./Figures/pol2_hist_nolabels.epsc')
end
%% Plot the ser5ph histograms
%
rng(0)
figure(10)
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh/3, 3*yh/3]; % x,y, width, height
[x,n] = hist(reshape(ser5_traj.',1,[]),30);
x1 = histogram(reshape(ser5_traj.',1,[]),n,'Normalization','probability','FaceColor',[11, 252, 3]./256,'FaceAlpha',.2,'linewidth',.01,'edgecolor',[11, 252, 3]./256)%,'DisplayStyle','stairs')
hold on;
x2 = histogram(reshape(ser5_traj.',1,[]),n,'Normalization','probability','FaceColor','none','FaceAlpha',.2,'linewidth',3,'edgecolor',[11, 252, 3]./256,'DisplayStyle','stairs')
x3 = histogram(ser5_data ,n,'Normalization','probability','FaceColor',[218, 51, 255]./256,'FaceAlpha',.2,'linewidth',.01,'edgecolor',[218, 51, 255]./256)%,'DisplayStyle','stairs')
x4 = histogram(ser5_data ,n,'Normalization','probability','FaceColor','none','FaceAlpha',.2,'linewidth',3,'edgecolor',[218, 51, 255]./256,'DisplayStyle','stairs')
%a = legend(gca,[x1,x3], {'Model','Data'},'Location','Best')
%set(a, 'Box', 'off');
ylim([0,.15])
xlim([-1.3,1.5])
set (gca ,'FontSize',fntsize,'FontName', 'Arial','Xcolor',global_color,'Ycolor',global_color);
set(a,'TextColor',global_color);
set(gca,'linewidth',2)
if save_eps
saveas(gca, './Figures/ser5hist.epsc')
end
set(gca,'YTickLabel',[],'XtickLabel',[])
if save_eps
saveas(gca,'./Figures/SER5_dist_nolabels.epsc')
end
%% Plot the mRNA comparison histograms
rng(0)
figure(12)
fig1= gcf;
fig1.PaperUnits = 'centimeters';
fig1.PaperPosition = [0, 0, xh/3, 3*yh/3]; % x,y, width, height
[x,n] = hist(mrnai_traj,30)
x1 = histogram(mrnai_traj,n,'Normalization','probability','FaceColor',[11, 252, 3]./256,'FaceAlpha',.2,'linewidth',3,'edgecolor',[11, 252, 3]./256)%'DisplayStyle','stairs')
hold on;