diff --git a/Model equations.txt b/Model equations.txt
index f58299c9c84698242bd6234e28a8978c5fa90ab6..1ece3289585364a8937194c57dc3f7c5a2662e46 100644
--- a/Model equations.txt
+++ b/Model equations.txt
@@ -1,8 +1,8 @@
********** MODEL NAME
-LipolysisModel
-
+lipolysis
********** MODEL NOTES
NOTE: This is just a copy of the lipolysis model.
+
********** MODEL STATES
d/dt(BETA) = -v4a + v4b
d/dt(BETAa) = v4a - v4b
@@ -21,19 +21,19 @@ d/dt(Gly) = v8a - v8b
d/dt(FFA) = 3*v8a - v8c
BETA(0) = 80
-BETAa(0) = 5
+BETAa(0) = 20
ALPHA(0) = 80
ALPHAa(0) = 20
-AC(0) = 75
-ACa(0) = 25
-PKB(0) = 20
-PKBp(0) = 80
-PDE3B(0) = 20
-PDE3Ba(0) = 80
-cAMP(0) = 1
-HSL(0) = 85
-HSLp(0) = 15
-Gly(0) = 220
+AC(0) = 80
+ACa(0) = 20
+PKB(0) = 80
+PKBp(0) = 20
+PDE3B(0) = 80
+PDE3Ba(0) = 20
+cAMP(0) = 0
+HSL(0) = 80
+HSLp(0) = 20
+Gly(0) = 0
FFA(0)= 0
********** MODEL PARAMETERS
kdrift=0
@@ -43,6 +43,7 @@ k4b = 0.12
k3b = 1e-5
k3a = 0.0004
+k3a2 = 1
k5a = 0.0025
k5b = 4.6
@@ -52,15 +53,16 @@ k1b=1
k2a = 2.1
k2b = 7.3
-k6basal = 3.7
k6a = 0.2
k6b = 0.05
-k6b2 = 0.02
k7a = 0.3
k7b = 0.07
k8a = 20
k8c = 1 % reesterification parameter
k8b = 1e-3
+
+phe_effect = 1
+
isoscale = 10
min3=0
@@ -74,7 +76,7 @@ n3=8
n1=8
n2=1
-diab = 1
+diab = 0
phe = 0
adr = 0
@@ -83,36 +85,34 @@ insulin = 37.2
********** MODEL VARIABLES
-y_alpha=ALPHAa
-y_PDE3Ba=PDE3Ba
y_PKB=PKBp
-y_cAMP=cAMP
-y_Glycerol=Gly-kdrift*time
y_HSL=HSLp
+y_Glycerol=Gly-kdrift*time
y_FFA=FFA-kdrift*time
-IR_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3);
-IR_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
-IR_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2);
+Ins_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3);
+Ins_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
+Ins_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2);
********** MODEL REACTIONS
-v1a = (k1a*cAMP+k1a2*IR_1)*PKB
-v1b = k1b*PKBp
-v2a = k2a*PKBp*PDE3B
-v2b = k2b * PDE3Ba*IR_2
-v3a = (k3a*IR_3)*adr*(1-phe)*ALPHA
-v3b = k3b * ALPHAa
v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
v4b = k4b * BETAa
-v5a = k5a * (BETAa) * AC
-v5b = k5b * (ALPHAa) * ACa
-v6a = k6basal + k6a*ACa
-v6b = (k6b*PDE3Ba + k6b2)*cAMP
+v3a = (k3a*Ins_3*adr+k3a2)*(1-phe_effect*phe)*ALPHA
+v3b = k3b * ALPHAa
+
+v1a = (k1a*cAMP+k1a2*Ins_1)*PKB
+v1b = k1b*PKBp
+v2a = k2a*PKBp*PDE3B
+v2b = k2b * PDE3Ba*Ins_2
+v5a = k5a * BETAa * AC
+v5b = k5b * ALPHAa * ACa
+v6a = k6a*ACa
+v6b = k6b*PDE3Ba*cAMP
v7a = k7a*cAMP*HSL
v7b = k7b * HSLp
v8a = k8a * HSLp
v8b = k8b * Gly
-v8c = (k8b+k8c*diab) * FFA
+v8c = (k8b+k8c*(1-diab)) * FFA
********** MODEL FUNCTIONS
********** MODEL EVENTS
diff --git a/Model equations_collapsed.txt b/Model equations_collapsed.txt
deleted file mode 100644
index ae5f93afa49a122d7519cb3d3fc5fbf1cfcc0ddc..0000000000000000000000000000000000000000
--- a/Model equations_collapsed.txt
+++ /dev/null
@@ -1,125 +0,0 @@
-********** MODEL NAME
-simple
-
-********** MODEL NOTES
-NOTE: This is just a copy of the lipolysis model.
-In this version, the reactions have been collapsed into direct expressions in the ODEs.
-********** MODEL STATES
-d/dt(BETA) = -(k4a * (iso*isoscale + adr) + k4a2) * BETA + k4b * BETAa
-d/dt(BETAa) = (k4a * (iso*isoscale + adr) + k4a2) * BETA - k4b * BETAa
-d/dt(ALPHA) = -(k3a*IR)*adr*(1-phe)*ALPHA + k3b * ALPHAa
-d/dt(ALPHAa) = (k3a*IR)*adr*(1-phe)*ALPHA - k3b * ALPHAa
-d/dt(AC) = -k5a * (BETAa) * AC + k5b * (ALPHAa) * ACa
-d/dt(ACa) = k5a * (BETAa) * AC - k5b * (ALPHAa) * ACa
-d/dt(PKB) = -(k1a*cAMP+k1a2*IR_1)*PKB + k1b*PKBp
-d/dt(PKBp) = (k1a*cAMP+k1a2*IR_1)*PKB - k1b*PKBp
-d/dt(PDE3B) = -k2a*PKBp*PDE3B + k2b * PDE3Ba*IR_2
-d/dt(PDE3Ba) = k2a*PKBp*PDE3B - k2b * PDE3Ba*IR_2
-d/dt(cAMP) = k6basal + k6a*ACa - (k6b*PDE3Ba + k6b2)*cAMP
-d/dt(HSL) = -k7a*cAMP*HSL + k7b * HSLp
-d/dt(HSLp) = k7a*cAMP*HSL - k7b * HSLp
-d/dt(Gly) = k8a * HSLp - k8b * Gly
-d/dt(FFA) = 3*k8a * HSLp - (k8b+k8c*diab) * FFA
-
-BETA(0) = 80
-BETAa(0) = 5
-ALPHA(0) = 80
-ALPHAa(0) = 20
-AC(0) = 75
-ACa(0) = 25
-PKB(0) = 20
-PKBp(0) = 80
-PDE3B(0) = 20
-PDE3Ba(0) = 80
-cAMP(0) = 1
-HSL(0) = 85
-HSLp(0) = 15
-Gly(0) = 220
-FFA(0)= 0
-********** MODEL PARAMETERS
-kdrift=0
-k4a = 62
-k4a2 = 0
-k4b = 0.12
-
-k3b = 1e-5
-k3a = 0.0004
-k5a = 0.0025
-k5b = 4.6
-
-k1a=1
-k1a2=1
-k1b=1
-
-k2a = 2.1
-k2b = 7.3
-k6basal = 3.7
-k6a = 0.2
-k6b = 0.05
-k6b2 = 0.02
-k7a = 0.3
-k7b = 0.07
-k8a = 20
-k8c = 1 % reesterification parameter
-k8b = 1e-3
-isoscale = 10
-
-min3=0
-min1=0
-min2=0
-
-EC503=4
-EC501=7
-EC504=1
-n3=8
-n1=8
-n2=1
-
-diab = 1
-
-phe = 0
-adr = 0
-iso = 0
-insulin = 37.2
-
-
-********** MODEL VARIABLES
-y_alpha=ALPHAa
-y_PDE3Ba=PDE3Ba
-y_PKB=PKBp
-y_cAMP=cAMP
-y_Glycerol=Gly-kdrift*time
-y_HSL=HSLp
-y_FFA=FFA-kdrift*time
-
-IR_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3);
-IR_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
-IR_2 = 100+(min2-100)/(1+(insulin/(10^EC504))^n2);
-
-********** MODEL REACTIONS
-v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
-v4b = k4b * BETAa
-
-v3a = (k3a*IR_3)*adr*(1-phe)*ALPHA
-v3b = k3b * ALPHAa
-v5a = k5a * (BETAa) * AC
-v5b = k5b * (ALPHAa) * ACa
-
-v1a = (k1a*cAMP+k1a2*IR_1)*PKB
-v1b = k1b*PKBp
-
-v2a = k2a*PKBp*PDE3B
-v2b = k2b * PDE3Ba*IR_2
-v6a = k6basal + k6a*ACa
-v6b = (k6b*PDE3Ba + k6b2)*cAMP
-v7a = k7a*cAMP*HSL
-v7b = k7b * HSLp
-v8a = k8a * HSLp
-v8b = k8b * Gly
-v8c = (k8b+k8c*diab) * FFA
-********** MODEL FUNCTIONS
-
-********** MODEL EVENTS
-
-********** MODEL MATLAB FUNCTIONS
-
diff --git a/PlotAllFigures.m b/PlotAllFigures.m
new file mode 100644
index 0000000000000000000000000000000000000000..c91ec46bc48b8167dca4a6e794faf8245b9f963b
--- /dev/null
+++ b/PlotAllFigures.m
@@ -0,0 +1,51 @@
+% This file allow you to generate all the figures from the paper.
+% For it to work, please change the current directory in MATLAB to the directory where the
+% files are saved.
+% If this file is run from the wrong directory, it will attempt to switch
+% to the correct one, with permission from the user.
+
+
+if ~(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'))
+ cdChoice=input('Warning! You are likely running the file from the wrong directory.\nDo you want to attempt to switch to the correct directory automatically?\n 1. yes\n 2. no\nChoice (1 or 2): ','s');
+ if cdChoice == 1
+ cd(fileparts(mfilename('fullpath')))
+ elseif cdChoice == 2
+ disp('Warning! The script will most likely not run from this directory.')
+ disp('If you encounter errors, please change the directory to the folder with the files.' )
+ else
+ disp('Invalid choice')
+ end
+end
+assert(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'),'Error! Likely running from the wrong directory.')
+currentDir = pwd;
+
+try
+ close all
+ fprintf('\nThis version plots all the figures at the original resolution.')
+ disp('This will take a while.')
+ disp('The figures will be finished in batches, but all figures will be available when the script is done.')
+ disp('Note that figures numbered 5X corresponds to supplementary figures SX.')
+ choice = input('\npress enter to continue.');
+
+ run('./Scripts and data/Setup.m')
+
+ res = 0.01;
+
+ cd('Scripts and data')
+ fprintf('\n\nRecreating the parameter identifiability figure (Fig. 2)\n')
+ PlotPL
+ fprintf('\n\nRecreating all plots related to figures before the addition of diabetes (Fig. 3, 4, S1)\n')
+ PlotUncertainty(0,res)
+ fprintf('\n\nRecreating the effects of removing insulin actions (Fig. 5)\n')
+ PlotInsulinActions
+ fprintf('\n\nRecreating all figure after diabetes was introduced (Fig. 6, 7, S3, S4)\n')
+ PlotUncertainty(1, res)
+ fprintf('\n\nRecreating the best agreement when insulin action 3 was removed (Fig. S2)\n')
+ PlotUncertainty(0,res, 'lipolysis_noIns3')
+
+ cd(currentDir)
+catch err
+ disp('Something went wrong.')
+ cd(currentDir)
+ rethrow(err)
+end
diff --git a/PlotAllFigures_fast.m b/PlotAllFigures_fast.m
new file mode 100644
index 0000000000000000000000000000000000000000..ce5260b44d6bd296ade3dd49c341649a073f1b0b
--- /dev/null
+++ b/PlotAllFigures_fast.m
@@ -0,0 +1,51 @@
+% This file allow you to generate all the figures from the paper.
+% For it to work, please change the current directory in MATLAB to the directory where the
+% files are saved.
+% If this file is run from the wrong directory, it will attempt to switch
+% to the correct one, with permission from the user.
+
+
+if ~(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'))
+ cdChoice=input('Warning! You are likely running the file from the wrong directory.\nDo you want to attempt to switch to the correct directory automatically?\n 1. yes\n 2. no\nChoice (1 or 2): ','s');
+ if cdChoice == 1
+ cd(fileparts(mfilename('fullpath')))
+ elseif cdChoice == 2
+ disp('Warning! The script will most likely not run from this directory.')
+ disp('If you encounter errors, please change the directory to the folder with the files.' )
+ else
+ disp('Invalid choice')
+ end
+end
+assert(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'),'Error! Likely running from the wrong directory.')
+currentDir = pwd;
+
+try
+ close all
+ fprintf('\nThis version plots all the figures at a low resolution.\n')
+ disp('This will be faster but less accurate.')
+ disp('The figures will be finished in batches, but all figures will be available when the script is done.')
+ disp('Note that figures numbered 5X corresponds to supplementary figures SX.')
+ choice = input('\npress enter to continue.');
+
+ run('./Scripts and data/Setup.m')
+
+ res = 1;
+
+ cd('Scripts and data')
+ fprintf('\n\nRecreating the parameter identifiability figure (Fig. 2)\n')
+ PlotPL
+ fprintf('\n\nRecreating all plots related to figures before the addition of diabetes (Fig. 3, 4, S1)\n')
+ PlotUncertainty(0,res)
+ fprintf('\n\nRecreating the effects of removing insulin actions (Fig. 5)\n')
+ PlotInsulinActions
+ fprintf('\n\nRecreating all figure after diabetes was introduced (Fig. 6, 7, S3, S4)\n')
+ PlotUncertainty(1, res)
+ fprintf('\n\nRecreating the best agreement when insulin action 3 was removed (Fig. S2)\n')
+ PlotUncertainty(0,res, 'lipolysis_noIns3')
+
+ cd(currentDir)
+catch err
+ disp('Something went wrong.')
+ cd(currentDir)
+ rethrow(err)
+end
diff --git a/PlotFigures.m b/PlotFigures.m
deleted file mode 100644
index 13b786842aed54b609a7fb39178afa59a82fb635..0000000000000000000000000000000000000000
--- a/PlotFigures.m
+++ /dev/null
@@ -1,56 +0,0 @@
-% This file allow you to generate all the figures from the paper.
-% For it to work, please change the current directory in MATLAB to the directory where the
-% files are saved.
-% If this file is run from the wrong directory, it will attempt to switch
-% to the correct one, with permission from the user.
-
-
-if ~(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'))
- cdChoice=input('Warning! You are likely running the file from the wrong directory.\nDo you want to attempt to switch to the correct directory automatically?\n 1. yes\n 2. no\nChoice (1 or 2): ','s');
- if cdChoice == 1
- cd(fileparts(mfilename('fullpath')))
- elseif cdChoice == 2
- disp('Warning! The script will most likely not run from this directory.')
- disp('If you encounter errors, please change the directory to the folder with the files.' )
- else
- disp('Invalid choice')
- end
-end
-assert(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'),'Error! Likely running from the wrong directory.')
-currentDir = pwd;
-
-try
- run('./Scripts and data/Setup.m')
- choice = input('\n\n\nWhich figures do you want to plot? \n 1. Figure 2 and 3\n 2. Figure 4.\n 3. Figure 5, 6 and S1. \nChoice (1, 2 or 3): ');
- choice2 = input('\nWhat resolution do you want? \nHigh resolution (as in the papers) or low resolution? \nWarning: High resolution will take quite a long time to simulate. \n 1. High resolution\n 2. Low resolution\nChoice (1 or 2): ');
-
- if ~ismember(choice,[1 2 3]) && ~ismember(choice2,[1 2])
- error('Invalid choice of plots. Invalid choice of resolution')
- elseif ~ismember(choice,[1 2 3]) && ismember(choice2,[1 2])
- error('Invalid choice of plots. Valid choice of resolution')
- elseif ismember(choice,[1 2 3]) && ~ismember(choice2,[1 2])
- error('Valid choice of plots. Invalid choice of resolution')
- elseif choice2==1
- res=0.01;
- elseif choice2==2
- res=1;
- else
- error('Invalid choice.')
- end
-
- cd('Scripts and data')
- if choice == 1
- PlotUncertainty(0,0,res)
- elseif choice == 2
- PlotUncertainty(0,1, res)
- elseif choice == 3
- PlotUncertainty(1,0, res)
- else
- disp('Invalid choice if plots')
- end
- cd(currentDir)
-catch err
- disp('Something went wrong.')
- cd(currentDir)
- rethrow(err)
-end
diff --git a/PlotSpecificFigures.m b/PlotSpecificFigures.m
new file mode 100644
index 0000000000000000000000000000000000000000..60e82924fe6e6b74529f9e9773c02496a05d243c
--- /dev/null
+++ b/PlotSpecificFigures.m
@@ -0,0 +1,77 @@
+% This file allow you to generate all the figures from the paper.
+% For it to work, please change the current directory in MATLAB to the directory where the
+% files are saved.
+% If this file is run from the wrong directory, it will attempt to switch
+% to the correct one, with permission from the user.
+
+
+if ~(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'))
+ cdChoice=input('Warning! You are likely running the file from the wrong directory.\nDo you want to attempt to switch to the correct directory automatically?\n 1. yes\n 2. no\nChoice (1 or 2): ','s');
+ if cdChoice == 1
+ cd(fileparts(mfilename('fullpath')))
+ elseif cdChoice == 2
+ disp('Warning! The script will most likely not run from this directory.')
+ disp('If you encounter errors, please change the directory to the folder with the files.' )
+ else
+ disp('Invalid choice')
+ end
+end
+assert(exist('./Model equations.txt','file') && exist('./Scripts and data','dir'),'Error! Likely running from the wrong directory.')
+currentDir = pwd;
+
+try
+ close all
+ run('./Scripts and data/Setup.m')
+ fprintf('\n\nWhich figures do you want to plot?\n')
+ disp('1. Parameter identifiability (Fig. 2)')
+ disp('2. All figures for model agreement before diabetes was included (Fig. 3, 4, S1)')
+ disp('3. Effects of removing insulin actions (Fig. 5)')
+ disp('4. All figure after diabetes was introduced (Fig. 6, 7, S3, S4)')
+ disp('5. Best agreement when insulin action 3 was removed (Fig. S2)')
+ disp('Note that figures numbered 5X corresponds to supplementary figures SX.')
+ choice = input('\nChoice (1, 2, 3, 4 or 5): ');
+
+ if ~ismember(choice, [1,3,5])
+ fprintf('\n\nWhat resolution do you want?\n')
+ disp('High resolution (as in the papers) or low resolution (faster)?')
+ disp('1. High resolution')
+ disp('2. Low resolution')
+ choice2 = input('\nChoice (1 or 2): ');
+ else
+ choice2 = 1;
+ end
+ fprintf('\n\n')
+ if ~ismember(choice,[1 2 3 4 5]) && ~ismember(choice2,[1 2])
+ error('Invalid choice of plots. Invalid choice of resolution')
+ elseif ~ismember(choice,[1 2 3 4 5]) && ismember(choice2,[1 2])
+ error('Invalid choice of plots. Valid choice of resolution')
+ elseif ismember(choice,[1 2 3 4 5]) && ~ismember(choice2,[1 2])
+ error('Valid choice of plots. Invalid choice of resolution')
+ elseif choice2==1
+ res=0.01;
+ elseif choice2==2
+ res=1;
+ else
+ error('Invalid choice.')
+ end
+
+ cd('Scripts and data')
+ if choice ==1
+ PlotPL
+ elseif choice == 2
+ PlotUncertainty(0,res)
+ elseif choice == 3
+ PlotInsulinActions
+ elseif choice == 4
+ PlotUncertainty(1, res)
+ elseif choice == 5
+ PlotUncertainty(0,res, 'lipolysis_noIns3')
+ else
+ disp('Invalid choice if plots')
+ end
+ cd(currentDir)
+catch err
+ disp('Something went wrong.')
+ cd(currentDir)
+ rethrow(err)
+end
diff --git a/README.md b/README.md
index 48f8a9b299a998664ca514a65fba82c59df3ad72..3a14aa24feac36c1dfb766b1440f8c4b329676d5 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,23 @@
-# A multi-level model analysis of lipolysis and fatty acid release from adipocytes *in vitro* and from adipose tissue *in vivo*
+# A systems biology analysis of lipolysis and fatty acid release from adipocytes *in vitro* and from adipose tissue *in vivo*
-To generate the plots:
+This repository corresponds to the results and analysis used in the paper: "A systems biology analysis of lipolysis and fatty acid release from adipocytes *in vitro* and from adipose tissue *in vivo*".
+A [preprint version of the paper is available at bioRxiv.](https://www.biorxiv.org/content/10.1101/2020.12.18.423229v1)
+
+### Abstract
+Lipolysis and the release of fatty acids to supply energy to other organs, such as between meals, during exercise, and starvation, are fundamental functions of the adipose tissue. The intracellular lipolytic pathway in adipocytes is activated by adrenaline and noradrenaline, and inhibited by insulin. Circulating fatty acids are elevated in type 2 diabetic individuals. The mechanisms behind this elevation are not fully known, and to increase the knowledge a link between the systemic circulation and intracellular lipolysis is key. However, data on lipolysis and knowledge from *in vitro* systems have not been linked to corresponding *in vivo* data and knowledge *in vivo*. Here, we use mathematical modelling to provide such a link. We examine mechanisms of insulin action by combining *in vivo* and *in vitro* data into an integrated mathematical model that can explain all data. Furthermore, the model can describe independent data not used for training the model. We show the usefulness of the model by simulating new and more challenging experimental setups *in silico*, e.g. the extracellular concentration of fatty acids during an insulin clamp, and the difference in such simulations between individuals with and without type 2 diabetes. Our work provides a new platform for model-based analysis of adipose tissue lipolysis, under both non-diabetic and type 2 diabetic conditions.
+
+## Required software
+These scripts are implemented in MATLAB (tested on R2020a), and requires a MATLAB compatible C-compiler. The easiest way to check if a valid compiler is available is to run `mex -setup` in MATLAB and check if it finds a compiler. For more information about compatible compilers, refer to [MathWorks support page](https://se.mathworks.com/support/requirements/supported-compilers.html).
+
+## How to recreate the plots from the paper:
1. Download the files (as .zip or through git)
2. If downloaded as .zip, extract the files
3. Open MATLAB (tested on R2020a)
4. Change folder in MATLAB to the folder with the files
-5. Run the script `PlotFigures`
-6. Follow the instructions in the MATLAB command window to select which plot to generate.
+5. To plot specific figures:
+ * Run either of the script `PlotAllFigures` or `PlotAllFigures_fast` to plot all figures. Note that the fast version runs with a lower resolution than the one used in the paper to save time.
+ * Run the script `PlotSpecificFigures`, and follow the instructions in the MATLAB command window to select specific plots to recreate.
+
+
+Note that generating high resolution plots will take some time. A low resolution faster option is available to save time.
-Note that generating high resolution plots will take some time. A low resolution option is available to save time. Again, follow on screen instructions.
-All choices should be entered as integers, e.g. `2`
diff --git a/Scripts and data/ConcatenateTableColums.m b/Scripts and data/ConcatenateTableColums.m
index 55500c597a2ed2a6772f58e8f016f6311a073520..ab30ab508d711f3b3a032ad48780e0bb17baf0fd 100644
--- a/Scripts and data/ConcatenateTableColums.m
+++ b/Scripts and data/ConcatenateTableColums.m
@@ -2,8 +2,8 @@
function [t]=ConcatenateTableColums(t1,t2)
% FUNCTION [t]=CONCATENATETABLECOLUMNS(t1,t2)
%The fuction takes two tables, and combines them columnwise.
-% Adds shared variables from t2 to t1. In practice, all columns in t2 which is also t1 is added.
-% Any column not in t1 will be ignored.
+% Adds shared variables from t2 to t1. In practice, all columns in t2 which is also t1 is added.
+% Any column not in t1 will be ignored.
t=t1;
for i = 1:length(t1.Properties.VariableNames)
var=t.Properties.VariableNames{i};
diff --git a/Scripts and data/CostDR.m b/Scripts and data/CostDR.m
deleted file mode 100644
index f478a699d119dee3e2d6eaa17f7c0a0e7986ff2a..0000000000000000000000000000000000000000
--- a/Scripts and data/CostDR.m
+++ /dev/null
@@ -1,73 +0,0 @@
-function [cost_tot, cost, responses]=CostDR(model, param, expInd, stimulus, data, diab, doDiabetes, printCost)
-if nargin <7, doDiabetes=0; end
-try
-
- [responses,~, basal]=simulateDR(model, param, expInd, doDiabetes, diab, stimulus);
-
- if any(abs(basal{:,:})<1e-3)
- penalty=sum(1./abs(basal{:,:}));
- else
- penalty=0;
- end
-
- costGlycerol=0;
- costcAMP=0;
- costFFA=0;
- costHSL=0;
- costPKB=0;
- costFFADiabetes=0;
-
- residuals=(responses.Normal{:,'Glycerol'}-data.InVitro.Glycerol.Mean).^2./data.InVitro.Glycerol.SEM.^2;
- costGlycerol=sum(residuals(~any(isnan(data.InVitro.Glycerol{:,3:4}),2)));
-
- residuals=(responses.Normal{:,'FFA'}-data.InVitro.FFA.Mean).^2./data.InVitro.FFA.SEM.^2;
- costFFA=sum(residuals(~any(isnan(data.InVitro.FFA{:,3:4}),2)));
-
- ind=ismember(responses.Normal{:,{'Ins','Iso'}},data.InVitro.PKB{:,{'Ins','Iso'}},'rows');
- residuals=(responses.Normal{ind,'PKB'}-data.InVitro.PKB.Mean).^2./data.InVitro.PKB.SEM.^2;
- costPKB=sum(residuals(~any(isnan(data.InVitro.PKB{:,3:4}),2)));
-
- if doDiabetes
- residuals=(responses.Normal{:,'HSL'}-data.InVitro.HSL.Mean).^2./data.InVitro.HSL.SEM.^2;
- costHSL=sum(residuals(~any(isnan(data.InVitro.HSL{:,3:4}),2)));
-
- residuals=(responses.Diabetes{:,'FFA'}-data.InVitro_diabetes.FFA.Mean).^2./data.InVitro_diabetes.FFA.SEM.^2;
- costFFADiabetes=sum(residuals(~any(isnan(data.InVitro_diabetes.FFA{:,3:4}),2)));
- end
-
- cost=costGlycerol+costFFA+costPKB+costcAMP+costHSL+costFFADiabetes;
- cost_tot=cost+penalty;
-
- %% Costs below here are not used when estimating.
- residuals=(responses.Normal{:,'HSL'}-data.InVitro.HSL.Mean).^2./data.InVitro.HSL.SEM.^2;
- costHSL=sum(residuals(~any(isnan(data.InVitro.HSL{:,3:4}),2)));
-
- residuals=(responses.Normal{:,'cAMP'}-data.InVitro.cAMP.Mean).^2./data.InVitro.cAMP.SEM.^2;
- costcAMP=sum(residuals(~any(isnan(data.InVitro.cAMP{:,3:4}),2)));
-
- if ~doDiabetes
- costFFADiabetes = nan;
- end
-catch
- cost=1e90; %returns a "large" error.
- cost_tot=1e91;
- costGlycerol=inf;
- costcAMP=inf;
- costFFA=inf;
- costHSL=inf;
- costPKB=inf;
- responses=[];
-end
-
-if printCost
- fprintf('DR, Glycerol: %.2f\n',costGlycerol)
- fprintf('DR, FFA: %.2f\n',costFFA)
- fprintf('DR, HSL: %.2f\n',costHSL)
- fprintf('DR, cAMP: %.2f\n',costcAMP)
- fprintf('DR, PKB: %.2f\n',costPKB)
- fprintf('DR, FFA-diabetes: %.2f\n',costFFADiabetes)
-
-end
-
-end
-
diff --git a/Scripts and data/CostInVitro.m b/Scripts and data/CostInVitro.m
new file mode 100644
index 0000000000000000000000000000000000000000..1eab486ca2c4b11a23a914729b7ab11913f31c2b
--- /dev/null
+++ b/Scripts and data/CostInVitro.m
@@ -0,0 +1,47 @@
+function [cost, responses, costHSL]=CostInVitro(model, param, expInd, stimulus, data, diab, doDiabetes)
+if nargin <7, doDiabetes=0; end
+try
+
+ [responses, steady, ~, basal]=simulateInVitro(model, param, expInd, diab, stimulus);
+
+ if any(abs(basal{:,:})<1e-3)
+ penalty=sum(1./abs(basal{:,:}))+50;
+ else
+ penalty = 0;
+ end
+
+ costHSL=0;
+ costReesterificationDiab = 0;
+
+ residuals=(responses.Normal{:,'Glycerol'}-data.InVitro.Glycerol.Mean).^2./data.InVitro.Glycerol.SEM.^2;
+ costGlycerol=sum(residuals(~any(isnan(data.InVitro.Glycerol{:,3:4}),2))); %only som residuals where
+
+ residuals=(responses.Normal{:,'FFA'}-data.InVitro.FFA.Mean).^2./data.InVitro.FFA.SEM.^2;
+ costFFA=sum(residuals(~any(isnan(data.InVitro.FFA{:,3:4}),2))); %only som residuals where
+
+ ind=ismember(responses.Normal{:,{'Ins'}},data.InVitro.PKB{:,{'Ins'}},'rows');
+ residuals=(responses.Normal{ind,'PKB'}-data.InVitro.PKB.Mean).^2./data.InVitro.PKB.SEM.^2;
+ costPKB=sum(residuals(~any(isnan(data.InVitro.PKB{:,3:4}),2))); %only som residuals where%
+
+ if doDiabetes
+ residuals=(responses.Normal{:,'HSL'}-data.InVitro.HSL.Mean).^2./data.InVitro.HSL.SEM.^2;
+ costHSL=sum(residuals(~any(isnan(data.InVitro.HSL{:,3:4}),2))); %only som residuals where%
+
+ ind=ismember(responses.Normal{:,{'Ins'}},data.InVitro_diabetes.Reesterification{:,{'Ins'}},'rows');
+ residuals=(responses.Diabetes{ind,'Reesterification'}-data.InVitro_diabetes.Reesterification.Mean).^2./data.InVitro_diabetes.Reesterification.SEM.^2;
+ costReesterificationDiab=sum(residuals(~any(isnan(data.InVitro_diabetes.Reesterification{:,3:4}),2))); %only som re
+ end
+
+ cost=costGlycerol+costFFA+costPKB+costHSL+costReesterificationDiab;
+ cost=cost+penalty+steady;
+
+catch err
+ cost=1e90; %returns a "large" error.
+ costHSL=inf;
+ responses=[];
+end
+
+
+
+end
+
diff --git a/Scripts and data/CostInVivo.m b/Scripts and data/CostInVivo.m
index 3bd3141c34f2028278087507b24cc4cb963e0329..91c5bd2111cdff20b2bb2900dc8de5f7e63acc84 100644
--- a/Scripts and data/CostInVivo.m
+++ b/Scripts and data/CostInVivo.m
@@ -1,35 +1,36 @@
-function [cost, responses] = CostInVivo(params, model, data, printCost)
-
+function [cost, responses] = CostInVivo(params, model, data, tol)
+if nargin<4, tol = 0; end
try
-ind = ismember(data.InVivo.Fig1.Time(1):data.InVivo.Fig1.Time(30), data.InVivo.Fig1.Time);
-
-[responses] = SimulateInVivo(params, model, data.InVivo);
-yh=responses.Gly.Fig1(ind);
-y=data.InVivo.Fig1.Mean;
-SEM=data.InVivo.Fig1.SEM;
-costFig1 = sum((yh-y).^2 ./SEM.^2);
-
-yh=[responses.Gly.Fig2Epi(ind); responses.Gly.Fig2Iso(ind)];
-y=[data.InVivo.Fig2Epi.Mean; data.InVivo.Fig2Iso.Mean];
-SEM=[data.InVivo.Fig2Epi.SEM; data.InVivo.Fig2Iso.SEM];
-costFig2 = sum((yh-y).^2 ./SEM.^2);
-
-yh=[responses.Gly.Fig3Epi(ind); responses.Gly.Fig3EpiPhe(ind)];
-y=[data.InVivo.Fig3Epi.Mean; data.InVivo.Fig3EpiPhe.Mean];
-SEM=[data.InVivo.Fig3Epi.SEM; data.InVivo.Fig3EpiPhe.SEM];
-costFig3 = sum((yh-y).^2 ./SEM.^2);
-
-cost=costFig1+costFig2+costFig3;
+ ind = ismember(data.Fig1.Time(1):data.Fig1.Time(30), data.Fig1.Time);
+
+ [responses, steady] = SimulateInVivo(params, model, data, 0, tol);
+
+ %% Fig1
+ yh=responses.Gly.Fig1(ind);
+ y=data.Fig1.Mean;
+ SEM=data.Fig1.SEM;
+ costFig1 = sum((yh-y).^2 ./SEM.^2);
+
+ %% Fig2
+ yh=[responses.Gly.Fig2Epi(ind); responses.Gly.Fig2Iso(ind)];
+ y=[data.Fig2Epi.Mean; data.Fig2Iso.Mean];
+ SEM=[data.Fig2Epi.SEM; data.Fig2Iso.SEM];
+ costFig2 = sum((yh-y).^2 ./SEM.^2);
+
+ %% Fig3
+ yh=responses.Gly.Fig3EpiPhe(ind);
+ y=data.Fig3EpiPhe.Mean;
+ SEM=data.Fig3EpiPhe.SEM;
+ costFig3 = sum((yh-y).^2 ./SEM.^2);
+
+ %% Total cost
+ cost=costFig1+costFig2+costFig3+steady;
catch err
cost=1e91; %returns a "large" error.
responses=[];
end
-if printCost
- fprintf('Fig1: %.4f\n', costFig1)
- fprintf('Fig2: %.4f\n', costFig2)
- fprintf('Fig3: %.4f\n', costFig3)
-end
+
end
diff --git a/Scripts and data/Data/expData.mat b/Scripts and data/Data/expData.mat
deleted file mode 100644
index 63f977e91dc602e3ff7833ef6f6aaa05ce06132d..0000000000000000000000000000000000000000
Binary files a/Scripts and data/Data/expData.mat and /dev/null differ
diff --git a/Scripts and data/Data/expDataDiabetes.mat b/Scripts and data/Data/expDataDiabetes.mat
index b1ffb8bb5c3a1edc14aa6ce2e6d44a4493be4d9c..e30b43b0534e3c29ec21e8f0eb6ff0ff519419f3 100644
Binary files a/Scripts and data/Data/expDataDiabetes.mat and b/Scripts and data/Data/expDataDiabetes.mat differ
diff --git a/Scripts and data/Data/expData_fig14.mat b/Scripts and data/Data/expData_fig14.mat
new file mode 100644
index 0000000000000000000000000000000000000000..9d8b85b95f51653ad5f88bae66d222c6d3d31e20
Binary files /dev/null and b/Scripts and data/Data/expData_fig14.mat differ
diff --git a/Scripts and data/FindBestParametersFile.m b/Scripts and data/FindBestParametersFile.m
new file mode 100644
index 0000000000000000000000000000000000000000..8ddc9679e2f948734570c591654a3c3452598870
--- /dev/null
+++ b/Scripts and data/FindBestParametersFile.m
@@ -0,0 +1,32 @@
+function [path,dirStruct] = FindBestParametersFile(pathToSearch, useSub, key)
+%FINDBESTPARAMETERSFILE finds the file containing the best parameter set
+%based on the name. Assumes that the cost is given in parenthesis.
+% [path,name] = FindBestParametersFile(path)
+if nargin<3, key='.'; end
+if nargin<2, useSub=0; end
+
+%%
+assert(exist(pathToSearch,'dir')>0,'Path does not exist. Are you in the right directory?');
+if useSub
+ pathToSearch=[pathToSearch '/**/*.mat'];
+end
+files=dir(pathToSearch);
+assert(length(files)>0,'No files to search in the provided path')
+names=string({files.name});
+if length(files)>1
+ files(cellfun(@isempty,regexp(names, key)))=[]; %removes all files not containing "key"
+
+ names=string({files.name});
+ values=double(regexprep(names,{'.*\(','\).*'},{'',''}));
+ assert(any(~isnan(values)),'No files contains valid names. Make sure the files has the cost in parenthesis, e.g opt(12.34)');
+ [~,bestInd]=min(values);
+ name=files(bestInd).name;
+ path=[files(bestInd).folder '/' name];
+ dirStruct=files(bestInd);
+else
+ assert(contains(names,key), "File does not contain provided key")
+ path =[files.folder '/' files.name];
+ dirStruct=files;
+end
+end
+
diff --git a/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseIQM.mexw64 b/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseIQM.mexw64
index 496ac55239fabbd87a873b2175909e9470ec3a33..67d9583c87e1a775ef861e5483d9fd338a335340 100644
Binary files a/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseIQM.mexw64 and b/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseIQM.mexw64 differ
diff --git a/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseSlopeIQM.mexw64 b/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseSlopeIQM.mexw64
index 431d0a7fdc1e5952555936e447225b7257cbf750..91ea043cd6166987cf88d079d3dccdcb84b7010e 100644
Binary files a/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseSlopeIQM.mexw64 and b/Scripts and data/IQM Tools/IQMlite/tools/interpolation/interpcseSlopeIQM.mexw64 differ
diff --git a/Scripts and data/IQM Tools/IQMlite/tools/optimization/auxiliary/isres/isrsort.mexw64 b/Scripts and data/IQM Tools/IQMlite/tools/optimization/auxiliary/isres/isrsort.mexw64
index 66242e4ee2c1adebdae04cc1e34e6cce32afd7de..13d77a2230386a32e3a0fb62af091c04e0229a3b 100644
Binary files a/Scripts and data/IQM Tools/IQMlite/tools/optimization/auxiliary/isres/isrsort.mexw64 and b/Scripts and data/IQM Tools/IQMlite/tools/optimization/auxiliary/isres/isrsort.mexw64 differ
diff --git a/Scripts and data/IQM Tools/IQMpro/tools/01-MEXmodels/CVODEMEX/lib/CVODEmex25.lib b/Scripts and data/IQM Tools/IQMpro/tools/01-MEXmodels/CVODEMEX/lib/CVODEmex25.lib
index 42dbc901be32b4cd2d25476f0fb7f8827a0d638d..3abfe488026813e0613b92ca9393d7589192c5d7 100644
Binary files a/Scripts and data/IQM Tools/IQMpro/tools/01-MEXmodels/CVODEMEX/lib/CVODEmex25.lib and b/Scripts and data/IQM Tools/IQMpro/tools/01-MEXmodels/CVODEMEX/lib/CVODEmex25.lib differ
diff --git a/Scripts and data/Init.m b/Scripts and data/Init.m
index 778c31df51cf85ad7b77464921f1c1cac1c84d10..c85467ba5a60404ff6fb1faaf03ee806508e15ee 100644
--- a/Scripts and data/Init.m
+++ b/Scripts and data/Init.m
@@ -1,5 +1,5 @@
-function [model,data, lb, ub, nParams,expInd, stimulus, dgf] = Init(modelName, doDiabetes, doMEX)
-if nargin<1; modelName='LipolysisModel'; end
+function [model,data, lb, ub, nParams,expInd, stimulus, dgf, opts] = Init(modelName, doDiabetes, doMEX)
+if nargin<1; modelName='lipolysis'; end
if nargin<2, doDiabetes=0; end
if nargin<3, doMEX=0; end
@@ -10,10 +10,7 @@ if doMEX
end
model=str2func(modelName);
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%% CREATE THE EXPDATA STRUCT
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
+%% Setup in vivo data
load('./Data/stichfig1.mat'); stichfig1.ins = stichfig1.ins * 6.945*1e-3; % converting from mU/L to nM
load('./Data/stichfig2.mat'); stichfig2.ins = stichfig2.ins * 6.945*1e-3;
load('./Data/stichfig3.mat'); stichfig3.ins = stichfig3.ins * 6.945*1e-3;
@@ -43,69 +40,95 @@ data.InVivo.Fig3EpiPhe.SEM=stichfig3.epiphe_se(1:30);
data.InVivo.Fig3EpiPhe.Time=stichfig3.time(1:30);
data.InVivo.Fig3EpiPhe.Ins=stichfig3.ins(1:30);
+%% Setup in vitro data
+load('Data/expData_fig14','expData')
+expData.FFA.SEM(end-2:end-1)= mean(expData.FFA.SEM([2:end-3 end]));
-load 'Data/expData'
expData.FFA.SEM(expData.FFA.SEM==0)=nan;
expData.Glycerol.SEM(expData.Glycerol.SEM==0)=nan;
expData.HSL.SEM(expData.HSL.SEM==0)=nan;
-expData.cAMP.SEM(expData.cAMP.SEM==0)=nan;
expData.PKB.SEM(expData.PKB.SEM==0)=nan;
-expData.FFA.Mean(2)=nan;
-expData.FFA.SEM(2)=nan;
-
data.InVitro.FFA=expData.FFA;
data.InVitro.Glycerol=expData.Glycerol;
-data.InVitro.cAMP=expData.cAMP;
data.InVitro.HSL=expData.HSL;
data.InVitro.PKB=expData.PKB;
-%% Diabetes data
-load 'Data/expDataDiabetes'
-data.InVitro_diabetes.FFA=expDataDiabetes.FFA;
+%% Setup diabetes data
+load('Data/expDataDiabetes','expDataDiabetes')
+data.InVitro_diabetes.FFA=expDataDiabetes.FFA;
+data.InVitro_diabetes.Glycerol=expDataDiabetes.Glycerol;
+data.InVitro_diabetes.HSL=expDataDiabetes.HSL;
+data.InVitro_diabetes.Reesterification=expDataDiabetes.reesterificationDiabetes;
+data.InVitro.Reesterification=expDataDiabetes.reesterification;
-%% DGF
-
-dgfcAMP=sum(~isnan(data.InVitro.cAMP.SEM));
+%% setup degrees of freedom
dgfGlycerol=sum(~isnan(data.InVitro.Glycerol.SEM));
dgfFFA=sum(~isnan(data.InVitro.FFA.SEM));
dgfHSL=sum(~isnan(data.InVitro.HSL.SEM));
dgfPKB=sum(~isnan(data.InVitro.PKB.SEM));
-dgfFFADiabetes = sum(~isnan(data.InVitro_diabetes.FFA.SEM));
+dgfReesterificationDiab=sum(~isnan(data.InVitro_diabetes.Reesterification.SEM));
dgfFig1Epi=sum(~isnan(data.InVivo.Fig1.SEM));
dgfFig2Epi=sum(~isnan(data.InVivo.Fig2Epi.SEM));
dgfFig2Iso=sum(~isnan(data.InVivo.Fig2Iso.SEM));
-dgfFig3Epi=sum(~isnan(data.InVivo.Fig3Epi.SEM));
dgfFig3Phe=sum(~isnan(data.InVivo.Fig3EpiPhe.SEM));
-
-dgfInVitro=dgfGlycerol+dgfFFA+dgfPKB;%+dgfcAMP
+dgfInVitro=dgfGlycerol+dgfFFA+dgfPKB;
if doDiabetes
- dgfInVitro=dgfInVitro+dgfFFADiabetes+dgfHSL;
+ dgfInVitro=dgfInVitro+dgfHSL+dgfReesterificationDiab;
end
-dgfInVivo=dgfFig1Epi+dgfFig2Epi+dgfFig2Iso+dgfFig3Epi+dgfFig3Phe;
+dgfInVivo=dgfFig1Epi+dgfFig2Iso+dgfFig3Phe+dgfFig2Epi;
-dgf=dgfInVivo+dgfInVitro-1-3; %-1 for drift parameter, -3 for scaling, -1 for experimental scaling
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%% THE OPTIMIZATION
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+dgf=dgfInVivo+dgfInVitro-1-6; %-1 for drift parameter, -6 for scaling
+%% Parameter bound setup
[pNames, ~] = IQMparameters(model);
expInd=sum(~cellfun(@isempty,(regexp(pNames,'^k.+'))));
-lb=repmat(log(1e-5), 1, expInd); % set lower bound for all "InVitro" parameters
-ub=-1*lb; % set upper bound for all "InVitro" paramters
-
-
-lb=[lb 0.1 0 0 0 -10 log10(0.5) -10 0 0 0]; %p=0?
-ub=[ub 100 100 100 100 10 log10(1.1) 10 10 10 10];
+lb=repmat(log(1e-6), 1, expInd); % set lower bound for all "InVitro" parameters
+ub=repmat(log(1e6), 1, expInd); % set upper bound for all "InVitro" paramters
+
+lb=[lb 0.6 8 0 0 0 -5 log(0.5) -5 0.5 0.5 0.5];
+ub=[ub 1 12 8 20 20 3 log(1.1) 3 2 2 2];
+
+if contains(modelName, '_noIns1')
+ lb([28 31])=[];
+ ub([28 31])=[];
+elseif contains(modelName, '_noIns2')
+ lb([29 32])=[];
+ ub([29 32])=[];
+elseif contains(modelName, '_noIns3')
+ lb([27 30])=[];
+ ub([27 30])=[];
+end
nParams=length(lb);
%% setup stimulus
stimulus=table();
ins=unique([expData.FFA.Ins; expData.Glycerol.Ins]);
stimulus.Ins=[ins; 0];
-stimulus.Iso=[10*ones(height(stimulus)-1,1); 0];
+stimulus.Iso=[0.01*ones(height(stimulus)-1,1); 0]; % 10nm = 0.01 µM
+
+%% Setup optimization
+opts.ndiverse = 500; %'auto'; %100; %500; %5; %
+opts.maxtime = 750; % In cess this option will be overwritten
+opts.maxeval = 1e7;
+opts.log_var = [];
+
+opts.local.solver = 'dhc'; %'dhc'; %'fmincon'; %'nl2sol'; %'mix'; %
+opts.local.finish = opts.local.solver;
+opts.local.bestx = 0;
+opts.local.tol = 2;
+opts.local.iterprint = 1;
+
+opts.dim_refset = 'auto'; %
+
+if(strcmp(opts.local.solver,'fmincon'))
+ opts.local.use_gradient_for_finish = 1; %DW: provide gradient to fmincon
+else
+ opts.local.use_gradient_for_finish = 0; %DW: provide gradient to fmincon
+end
+opts.local.check_gradient_for_finish = 0; %DW: gradient checker
end
diff --git a/Scripts and data/NonIdentifiabilityBound.m b/Scripts and data/NonIdentifiabilityBound.m
new file mode 100644
index 0000000000000000000000000000000000000000..6d4b6ab25c7762e56ce9c267f998867f9a192d33
--- /dev/null
+++ b/Scripts and data/NonIdentifiabilityBound.m
@@ -0,0 +1,25 @@
+function [bound] = NonIdentifiabilityBound(paramName,bound, direction)
+if strcmp(direction,'min')
+ if regexp(paramName, '^k')
+ bound = log(exp(bound)/1e3);
+ elseif contains(paramName, 'EC50')
+ bound = -6;
+ else
+ bound = 0;
+ end
+else
+ if regexp(paramName, '^k')
+ bound = log(exp(bound)*1e3);
+ elseif strcmp(paramName, 'phe_effect')
+ bound = 1;
+ elseif strcmp(paramName, 'isoscale')
+ bound = 100;
+ elseif contains(paramName, 'min')
+ bound = 100;
+ elseif contains(paramName, 'EC50')
+ bound = 4;
+ elseif ismember(paramName,{'n1','n2','n3'})
+ bound = 4;
+ end
+end
+end
\ No newline at end of file
diff --git a/Scripts and data/Parameter sets (with diabetes)/PPL/[InVitro, FFA, max, 0.0001] lipolysis-opt(99.801), 210620-181317-NaN.mat b/Scripts and data/Parameter sets (with diabetes)/PPL/[InVitro, FFA, max, 0.0001] lipolysis-opt(99.801), 210620-181317-NaN.mat
new file mode 100644
index 0000000000000000000000000000000000000000..88cb9bfcd40d2f10be18aca0bf3ee69f8901ddbe
Binary files /dev/null and b/Scripts and data/Parameter sets (with diabetes)/PPL/[InVitro, FFA, max, 0.0001] lipolysis-opt(99.801), 210620-181317-NaN.mat differ
diff --git a/Scripts and data/Parameter sets (with diabetes)/PPL/[InVitro, FFA, max, 0.001] lipolysis-opt(96.906), 210620-181317-NaN.mat b/Scripts and data/Parameter sets (with diabetes)/PPL/[InVitro, FFA, max, 0.001] lipolysis-opt(96.906), 210620-181317-NaN.mat
new file mode 100644
index 0000000000000000000000000000000000000000..95c09da071067129f9fc84cba8c1fc50f0ca380a
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diff --git a/Scripts and data/Parameter sets/[HSL, cost, min, nan] lipolysis-opt(10.676), 210602-222523-971403550.mat b/Scripts and data/Parameter sets/[HSL, cost, min, nan] lipolysis-opt(10.676), 210602-222523-971403550.mat
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diff --git a/Scripts and data/Parameter sets/lipolysis, opt-eSS(130.82), 20210608-220548 1023059774.mat b/Scripts and data/Parameter sets/lipolysis, opt-eSS(130.82), 20210608-220548 1023059774.mat
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diff --git a/Scripts and data/Parameter sets/lipolysis_noIns1, opt-eSS(501.09), 20210611-141201 1046141009.mat b/Scripts and data/Parameter sets/lipolysis_noIns1, opt-eSS(501.09), 20210611-141201 1046141009.mat
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diff --git a/Scripts and data/Parameter sets/lipolysis_noIns2, opt-eSS(181.75), 20210611-161427 1046882385.mat b/Scripts and data/Parameter sets/lipolysis_noIns2, opt-eSS(181.75), 20210611-161427 1046882385.mat
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diff --git a/Scripts and data/Parameter sets/lipolysis_noIns3, opt-eSS(126.36), 20210615-110227 1079550108.mat b/Scripts and data/Parameter sets/lipolysis_noIns3, opt-eSS(126.36), 20210615-110227 1079550108.mat
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diff --git a/Scripts and data/PlotInVitro.m b/Scripts and data/PlotInVitro.m
index 4ca3d5213a8fca23248d36d1a170c836555ce353..67ba6f144fde8ba5b671bef90f59ed21c2e850a4 100644
--- a/Scripts and data/PlotInVitro.m
+++ b/Scripts and data/PlotInVitro.m
@@ -1,30 +1,25 @@
-function [] = PlotInVitro(responses, expData, experiments, id, xVar, posOverride)
+function [] = PlotInVitro(responses, expData, experiments, id, posOverride)
%PLOT_DOSE_RESPONSE Summary of this function goes here
% Detailed explanation goes here
-if nargin<5 || isempty(xVar)
- xVar='Ins';
-end
-if nargin<3 || isempty(experiments)
- experiments = fieldnames(responses);
-end
+if nargin<5, posOverride=0; end
-if nargin > 5 && ~isempty(posOverride)
- pos = posOverride;
- m=3;
- n=2;
-else
- if width(responses.Normal)==4
- m=2; n=2; pos=[1 2];
- elseif width(responses.Normal)==3
+
+if nargin<3 || isempty(experiments), experiments = fieldnames(responses); end
+
+if posOverride ~=0
+ m=3; n=2; pos = posOverride;
+elseif ismember('Diabetes', experiments)
+ m=2; n=2; pos=[1 2 3 4];
+elseif width(responses.Normal)==3
m=2; n=2; pos=[1];
- else
+elseif any(contains(responses.Normal.Properties.VariableNames, 'Ins_')) % for plotting only the input
+ [m,n] = CloseToSquare(width(responses.Normal)-2);
+ pos=1:width(responses.Normal)-2;
+else
m=3; n=2; pos=[2 4 6];
- end
-end
-if nargin > 5 && ~isempty(posOverride)
- pos = posOverride;
end
+
figure(id);
for i =1:length(experiments)
switch experiments{i}
@@ -39,51 +34,58 @@ for i =1:length(experiments)
case 'Diabetes'
c=[230 159 0]/256;
otherwise
- c=[0 0 0];
- end
-
- if strcmp(experiments{i}, 'Normal')
- dontPlotVars=[];
- elseif strcmp(experiments{i}, 'Diabetes')
- response=responses.(experiments{i});
- dontPlotVars={};
- else
- dontPlotVars={'IR','IR_2','IR_3'};
+ c=[0 0 0];
+
end
response=responses.(experiments{i});
- if ~isempty(dontPlotVars)
- response{:,dontPlotVars}=nan(size(response{:,dontPlotVars}));
- end
if isfield(expData,experiments{i})
data=expData.(experiments{i});
else
data=[];
end
- PlotExperiment(m,n,pos,response, data, c, xVar)
+ PlotExperiment(m,n,pos,response, data, c)
end
PlotInput(m,n,pos,responses.Normal)
set(figure(id), 'outerposition',[0 0 2560 1440], 'PaperType','a4')
end
-function []=PlotExperiment(m,n,pos,response, data, c, xVar)
+function []=PlotExperiment(m,n,pos,response, data, c)
set(0,'DefaultLineLineWidth',2)
for j=3:width(response)
variable=response.Properties.VariableNames{j};
- if isfield(data,variable)
- PlotSubplot(data.(variable), response(:,[1 2 j]),m,n,pos(j-2), c, xVar)
+ if strcmp(response.Properties.VariableNames{j},'u_C')
+ subplot(m,n,pos(j-2))
+ x=response{:,'u_cAMP'};
+ y=response{:,'u_C'};
+ x=reshape(x,numel(x),1);
+ y=reshape(y,numel(y),1);
+
+ [~,ind]=sort(x);
+ plot(x(ind), y(ind),'o')
else
- PlotSubplot([], response(:,[1 2 j]),m,n,pos(j-2), c, xVar)
+
+ if isequal(c, [230 159 0]/256) && ismember(response.Properties.VariableNames{j},{'PKB', 'HSL','Glycerol'})
+ l='--';
+ else
+ l='-';
+ end
+
+ if isfield(data,variable)
+ PlotSubplot(data.(variable), response(:,[1 2 j]),m,n,pos(j-2), c, l)
+ else
+ PlotSubplot([], response(:,[1 2 j]),m,n,pos(j-2), c, l)
+ end
end
end
end
-function []=PlotSubplot(data, sim, m,n,ind,c, xVar)
-x=sim.(xVar)*1e-9;
+function []=PlotSubplot(data, sim, m,n,ind,c, l)
+x=sim.Ins*1e-9;
if x(1)==0
x(1)=x(2)/4;
end
@@ -107,17 +109,17 @@ if size(y,2)==3
h3 = fill([x(end)/1.5 x(end)/1.5 x(end)*1.5 x(end)*1.5],[y(end, 2) y(end, 3) y(end, 3) y(end, 2) ], c);
set(h3,'facealpha',0.5,'edgealpha',0);
elseif size(y,2)>3
- plot([x(1)/1.5 x(1)*1.5],[y(1, :); y(1, :)],'-','Color',c,'linewidth', 2.5);
+ plot([x(1)/1.5 x(1)*1.5],[y(1, :); y(1, :)],l,'Color',c,'linewidth', 2.5);
hold on
- semilogx(x(2:end-1),y(2:end-1,:),'-','Color',c)
- plot([x(end)/1.5 x(end)*1.5],[y(end, :); y(end, :)],'-','Color',c,'linewidth', 2.5);
+ semilogx(x(2:end-1),y(2:end-1,:),l,'Color',c)
+ plot([x(end)/1.5 x(end)*1.5],[y(end, :); y(end, :)],l,'Color',c,'linewidth', 2.5);
end
if any(ismember(size(y,2),[1 3]))
- plot([x(1)/1.5 x(1)*1.5],[y(1, 1) y(1, 1)],'-','Color',c,'linewidth', 2.5);
+ plot([x(1)/1.5 x(1)*1.5],[y(1, 1) y(1, 1)],l,'Color',c,'linewidth', 2.5);
hold on
- plot(x(2:end-1), y(2:end-1,1), 'color', c,'linewidth', 2.5);
- plot([x(end)/1.5 x(end)*1.5],[y(end, 1) y(end, 1)],'-','Color',c,'linewidth', 2.5);
+ plot(x(2:end-1), y(2:end-1,1),l, 'color', c,'linewidth', 2.5);
+ plot([x(end)/1.5 x(end)*1.5],[y(end, 1) y(end, 1)],l,'Color',c,'linewidth', 2.5);
end
if ~isempty(data)
@@ -133,38 +135,59 @@ for i = 1:width(response)-2
subplot(m,n,pos(i))
axis('tight');
- xlabel('[Insulin] (M)')
- if contains(response.Properties.VariableNames{i+2},{'HSL', 'PKB'})
+ if contains (response.Properties.VariableNames{i+2},{'u_C'})
+ title(response.Properties.VariableNames{i+2})
+ set(gca,'XScale','log', 'XMinorTick','on','FontSize', 20)
+ axis('tight');
+ box off
+ return
+ elseif contains (response.Properties.VariableNames{i+2},{'u_'})
+ ylabel({sprintf('%s,',response.Properties.VariableNames{i+2})})
+ elseif contains(response.Properties.VariableNames{i+2},{'HSL', 'PKB'})
ylabel({sprintf('Phosphorylation of %s,',response.Properties.VariableNames{i+2}); '% of iso only'})
+ elseif contains(response.Properties.VariableNames{i+2},'IR_')
+ ylabel(['Response, ' response.Properties.VariableNames{i+2}])
+ elseif contains (response.Properties.VariableNames{i+2},{'Reesterification'})
+ ylabel('% Reesterification')
else
ylabel({sprintf('Released %s,',response.Properties.VariableNames{i+2}); '% of iso only'})
end
+ xlabel('[Insulin] (M)')
axsPatch=findobj(gca,'type','patch');
axsLine=findobj(gca,'type','line');
axsErrorbar = findobj(gca, 'type', 'errorbar');
- YData=[];
- if ~isempty(axsPatch)
- YData=[YData horzcat(axsLine.YData)];
- end
+
if ~isempty(axsLine)
- YData=[YData vertcat(axsPatch.YData)'];
+ YData=horzcat(axsLine.YData);
+ end
+ if ~isempty(axsPatch)
+ YData=vertcat(axsPatch.YData)';
end
if ~isempty(axsErrorbar)
- YData=[YData horzcat(axsErrorbar.YData)-horzcat(axsErrorbar.YNegativeDelta) horzcat(axsErrorbar.YData)+horzcat(axsErrorbar.YPositiveDelta)];
+ YData=[horzcat(axsErrorbar.YData)-horzcat(axsErrorbar.YNegativeDelta) horzcat(axsErrorbar.YData)+horzcat(axsErrorbar.YPositiveDelta)];
+ end
+
+ if contains (response.Properties.VariableNames{i+2},{'Reesterification'})
+ minimum = 10;
+ else
+ minimum = min(YData);
end
- minimum = min(YData);
maximum = max(YData);
x=response.Ins*1e-9;
x(1)=x(2)/4/1.5;
- plot(x([1 end-1]), [minimum-0.03*(maximum-minimum) minimum-0.03*(maximum-minimum)],'color', [0.7 0.7 0.7],'linewidth', 3) % iso stimulus bar
- plot(x([2 end-1]), [minimum-0.1*(maximum-minimum) minimum-0.1*(maximum-minimum) ],'color', [0 0 0],'linewidth', 3) % ins stimulus bar
+ plot(x([1 end-1]), [minimum-0.1*(maximum-minimum) minimum-0.1*(maximum-minimum)],'-','color', [0.7 0.7 0.7],'linewidth', 5) % iso stimulus bar
set(gca,'XScale','log', 'XMinorTick','on','FontSize', 20)
- axis('tight');
+ if contains (response.Properties.VariableNames{i+2},{'Reesterification'})
+ ylim([0, 100])
+ else
+ axis('tight');
+ end
a=axis;
axis([a(1), a(2), minimum-0.175*(maximum-minimum), a(4)])
+
box off
end
end
\ No newline at end of file
diff --git a/Scripts and data/PlotInVivo.m b/Scripts and data/PlotInVivo.m
index 3180adca76f482d3f55e1d9037a30becab67b6e7..b48dd8f5d6e4ffded37a49e3823fc50ebe083279 100644
--- a/Scripts and data/PlotInVivo.m
+++ b/Scripts and data/PlotInVivo.m
@@ -1,43 +1,48 @@
-function [] = PlotInVivo(data, sim, id, titleStr, vars, posOverride)
+function [] = PlotInVivo(data, sim, id, titleStr, vars, normalize, posOverride)
if nargin<4; titleStr=''; end
if nargin <5 || isempty(vars)
- vars=sim.Properties.VariableNames;
- vars(ismember(vars,'Time'))=[];
+ vars=sim.Properties.VariableNames;
+ vars(ismember(vars,'Time'))=[];
end
+if nargin <6 || isempty(normalize), normalize=0; end
+if nargin<7; posOverride=0; end
+fig=figure(id);
set(figure(id), 'outerposition',[0 0 2560 1440], 'PaperType','a4')
-nPlots=round(length(vars)/2);
-
-if nargin>5 && ~isempty(posOverride)
- m=3; n=2; pos=posOverride;
-else
- if contains(titleStr, 'Glycerol')
- m=3; n=2; pos=[1 3 5];
- else
+if posOverride ~=0
+ m=3; n=2; pos=[nan, nan, nan, posOverride];
+elseif any(contains(vars, 'Diab'))
m=2; n=2; pos=[1 2];
- end
+elseif any(strcmp(vars, 'Fig3Epi'))
+ m=3; n=2; pos=[1 3 3 5 5];
+else
+ m=4; n=2; pos=[1 3 5 nan 7];
+
end
+
for i=1:length(vars)
- switch vars{i}
- case 'Fig1', c=[86 180 233]/256; plt=1; leg='adr';
- case 'Fig1Diab', c=[213 94 0]/256; plt=1; leg='adr (T2D)';
- case 'Fig2EpiDiab', c=[213 94 0]/256; plt=pos(2); leg='adr (T2D)';
- case 'Fig2Epi', c = [86 180 233]/256; plt=pos(2); leg='adr';
- case 'Fig2Iso', c = [170 50 177]/256; plt=3; leg='iso';
- case 'Fig3Epi', c = [86 180 233]/256; plt=pos(end); leg='adr';
- case 'Fig3EpiPhe', c = [0 103 28]/256; plt=pos(end); leg='adr + phe';
- end
- if isfield(data,'InVivo')
- PlotExperiment(m,n,plt,data.InVivo.(vars{i}),sim(:,{'Time',vars{i}}), leg, c)
- else
- PlotExperiment(m,n,plt,[],sim(:,{'Time',vars{i}}), leg, c)
- end
+ switch vars{i}
+ case 'Fig1', c=[86 180 233]/256; plt=1; leg='adr';
+ case 'Fig1Diab', c=[213 94 0]/256; plt=1; leg='adr (T2D)';
+ case 'Fig2EpiDiab', c=[213 94 0]/256; plt=pos(2); leg='adr+ins (T2D)';
+ case 'Fig2Epi', c = [86 180 233]/256; plt=pos(2); leg='adr+ins';
+ case 'Fig2Iso', c = [170 50 177]/256; plt=pos(3); leg='iso+ins';
+ case 'Fig3Epi', c = [86 180 233]/256; plt=pos(4); leg='adr+ins';
+ case 'Fig3EpiPhe', c = [0 103 28]/256; plt=pos(5); leg='adr+phe+ins';
+ end
+ if isfield(data,'InVivo')
+ PlotExperiment(m,n,plt,data.InVivo.(vars{i}),sim(:,{'Time',vars{i}}), leg, c)
+ else
+ PlotExperiment(m,n,plt,[],sim(:,{'Time',vars{i}}), leg, c)
+ end
end
+pos = unique(pos(~isnan(pos)));
+nPlots=length(pos);
for i = 1:nPlots
- subplot(m,n,pos(i))
- PlotInput(m,n,pos(i), titleStr)
+ subplot(m,n,pos(i))
+ PlotInput(m,n,pos(i), titleStr)
end
end
@@ -49,18 +54,18 @@ x=sim{:,1}-15;
y=sim{:,2};
if size(y,2)>1 && isequal(y(:,2), y(:,3))
- y=y(:,2);
+ y=y(:,2);
end
if size(y,2)>1
- h = fill([x; flipud(x)], [y(:,2); flipud(y(:,3))],c,'HandleVisibility','off');
- set(h,'facealpha',0.5,'edgealpha',0);
+ h = fill([x; flipud(x)], [y(:,2); flipud(y(:,3))],c,'HandleVisibility','off');
+ set(h,'facealpha',0.5,'edgealpha',0);
end
plot(x, y(:,1), 'color', c,'linewidth', 2.5);
hold on
if isfield(data,'Mean')
- errorbar(data.Time-15, data.Mean, data.SEM, 'o','MarkerFaceColor', 'auto', 'linewidth', 2.5,'capsize',12, 'color', c, 'HandleVisibility','off')
+ errorbar(data.Time-15, data.Mean, data.SEM, 'o','MarkerFaceColor', 'auto', 'linewidth', 2.5,'capsize',12, 'color', c, 'HandleVisibility','off')
end
lgd=legend();
lgd.String{end}=leg;
@@ -76,38 +81,35 @@ axsPatch=findall(gca,'type','patch');
axsLine=findobj(gca,'type','line');
axsErrorbar = findall(gca, 'type', 'errorbar');
YData=[];
-if ~isempty(axsPatch)
- YData=[YData horzcat(axsLine.YData)];
-end
if ~isempty(axsLine)
- YData=[YData vertcat(axsPatch.YData)'];
+ YData=[YData horzcat(axsLine.YData)];
+end
+if ~isempty(axsPatch)
+ YData=[YData vertcat(axsPatch.YData)'];
end
if ~isempty(axsErrorbar)
- YData=[YData horzcat(axsErrorbar.YData)-horzcat(axsErrorbar.YNegativeDelta) horzcat(axsErrorbar.YData)+horzcat(axsErrorbar.YPositiveDelta)];
+ YData=[YData horzcat(axsErrorbar.YData)-horzcat(axsErrorbar.YNegativeDelta) horzcat(axsErrorbar.YData)+horzcat(axsErrorbar.YPositiveDelta)];
end
minimum = min(YData);
maximum = max(YData);
-plot([15 45]-10,[minimum-0.06*(maximum-minimum) minimum-0.06*(maximum-minimum)],'color',[0.85 0.85 0.85],'linewidth',3)
-plot([45 75]-10,[minimum-0.03*(maximum-minimum) minimum-0.03*(maximum-minimum)],'color',[0.7 0.7 0.7],'linewidth',3)
-h= plot([75 370]-10,[minimum-0.1*(maximum-minimum) minimum-0.1*(maximum-minimum)],'color',[0 0 0],'linewidth',3);
+plot([15 45]-10,[minimum-0.06*(maximum-minimum) minimum-0.06*(maximum-minimum)],'-', 'color',[0.85 0.85 0.85],'linewidth',5)
+plot([45 75]-10,[minimum-0.03*(maximum-minimum) minimum-0.03*(maximum-minimum)],'-','color',[0.7 0.7 0.7],'linewidth',5)
+
+h= plot([75 370]-10,[minimum-0.1*(maximum-minimum) minimum-0.1*(maximum-minimum)],'-','color',[0 0 0],'linewidth',5);
if plt==1
- set(h,'LineStyle','none')
+ set(h,'LineStyle','none')
end
-plot([15 45]+180-10,[minimum-0.06*(maximum-minimum) minimum-0.06*(maximum-minimum)],'color',[0.85 0.85 0.85],'linewidth',3)
-plot([45 75]+180-10,[minimum-0.03*(maximum-minimum) minimum-0.03*(maximum-minimum)],'color',[0.7 0.7 0.7],'linewidth',3)
+plot([15 45]+180-10,[minimum-0.06*(maximum-minimum) minimum-0.06*(maximum-minimum)],'-','color',[0.85 0.85 0.85],'linewidth',5)
+plot([45 75]+180-10,[minimum-0.03*(maximum-minimum) minimum-0.03*(maximum-minimum)],'-','color',[0.7 0.7 0.7],'linewidth',5)
axis('tight');
a=axis;
axis([a(1), a(2), minimum-0.175*(maximum-minimum), a(4)])
xlabel('Time (min)')
-if strcmp(titleStr,'FFA')
- ylabel({'Released FA','% of max response'})
-else
-ylabel(sprintf('Released %s (\\muM)', titleStr))
-end
+ylabel(sprintf('Released %s\n(\\muM)', titleStr))
set(gca,'FontSize', 20)
lgd.String=lgd0;
diff --git a/Scripts and data/PlotInsulinActions.m b/Scripts and data/PlotInsulinActions.m
new file mode 100644
index 0000000000000000000000000000000000000000..365da2a428eb8f61ca129453ec098b61f776e75b
--- /dev/null
+++ b/Scripts and data/PlotInsulinActions.m
@@ -0,0 +1,51 @@
+function [] = PlotInsulinActions()
+modelName = 'lipolysis';
+baseFolder = './Parameter sets' ;
+
+[model,data, lb, ub, nParams, expInd, stimulus, dgf] = Init(modelName, 0, 0);
+inVitroData.Normal=data.InVitro;
+
+
+stimulusHighRes=table();
+ins=log10(unique([data.InVitro.FFA.Ins; data.InVitro.Glycerol.Ins]));
+stimulusHighRes.Ins=[0 10.^(ins(2):.01:ins(end)) 0]';
+stimulusHighRes.Iso=[0.01*ones(height(stimulusHighRes)-1,1); 0]; %10 nM = 0.01 µM
+
+figure(5)
+clf
+%% Plot original model
+load(FindBestParametersFile(baseFolder, 1, [modelName ', opt-eSS']), 'optParam')
+
+diab=0;
+
+params=[exp(optParam(1:expInd)) optParam(expInd+1:end)];
+DR=simulateInVitro(model, params, expInd, diab, stimulusHighRes, 0);
+TS = SimulateInVivo(params, model, data.InVivo, 0, 0);
+DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso','Glycerol'}))=[];
+
+PlotInVivo(data, TS.Gly, 5,'Glycerol', {'Fig3Epi','Fig3EpiPhe'},0, [2,2])
+PlotInVitro(DR, inVitroData, {'Normal'}, 5, 1)
+
+%% Plot without action 1)
+params_no1 = params;
+params_no1(strcmp(IQMparameters(model),'EC501'))=inf;
+
+DR=simulateInVitro(model, params_no1, expInd, diab, stimulusHighRes, 0);
+DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso','Glycerol'}))=[];
+PlotInVitro(DR, inVitroData, {'Normal'}, 5, 3)
+
+%% Plot without action 2)
+params_no2 = params;
+params_no2(strcmp(IQMparameters(model),'EC502'))=inf;
+
+DR=simulateInVitro(model, params_no2, expInd, diab, stimulusHighRes, 0);
+DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso','Glycerol'}))=[];
+PlotInVitro(DR, inVitroData, {'Normal'}, 5, 5)
+%% Plot without action 3)
+params_no3 = params;
+params_no3(strcmp(IQMparameters(model),'EC503'))=inf;
+
+TS = SimulateInVivo(params_no3, model, data.InVivo, 0, 0);
+PlotInVivo(data, TS.Gly, 5,'Glycerol', {'Fig3Epi','Fig3EpiPhe'},0, [4,4])
+end
+
diff --git a/Scripts and data/PlotPL.m b/Scripts and data/PlotPL.m
new file mode 100644
index 0000000000000000000000000000000000000000..a2476757a7de6a5181cf722546c957098e4ddf7f
--- /dev/null
+++ b/Scripts and data/PlotPL.m
@@ -0,0 +1,78 @@
+function [] = PlotPL(modelName)
+
+
+if nargin<1; modelName='lipolysis'; end
+baseFolder='./Parameter sets';
+
+folder= 'Parameter sets/PL/';
+
+[model, data, lb, ub, nParams, expInd, stimulus, dgf] = Init(modelName, 0);
+pNames = IQMparameters(model);
+rejectlb = lb;
+rejectub = ub;
+for i = 1:nParams
+ rejectlb(i) = NonIdentifiabilityBound(pNames{i}, lb(i), 'min');
+ rejectub(i) = NonIdentifiabilityBound(pNames{i}, ub(i), 'max');
+end
+
+files=dir(sprintf('%s/**/*%s*.mat', folder, modelName));
+optParams=[];
+for i = fliplr(1:length(files))
+ load([files(i).folder '/' files(i).name],'optParam');
+ optParams(i,:)=optParam;
+end
+optParams=unique(optParams,'rows');
+%% Print bounds
+maxP = max(optParams)';
+maxP=[exp(maxP(1:expInd)); maxP(expInd+1:end)];
+maxP(27:29) = 10.^maxP(27:29);
+minP = min(optParams)';
+minP=[exp(minP(1:expInd)); minP(expInd+1:end)];
+minP(27:29) = 10.^minP(27:29);
+params=table(pNames(1:32),minP,maxP,'VariableNames',{'Parameters', 'Min','Max'});
+disp('Boundry for parameter uncertainty, presented in Table S1.')
+disp(params)
+%%
+x = 2*(optParams-rejectlb) ./ (rejectub-rejectlb)-1;
+
+minx = min(x);
+maxx=max(x);
+
+figure(2)
+clf
+plot([0 nParams],[1 1],'color',[0.6350 0.0780 0.1840])
+hold on
+plot([0 nParams],[-1 -1],'color', [0.6350 0.0780 0.1840])
+
+%%
+center = (maxx+minx)/2;
+bound = (maxx-minx)/2;
+h = errorbar(1:size(x,2),center, bound,'.','linewidth',3, 'color', 'k');
+h.CapSize = 20;
+xticks(1:size(x,2))
+xticklabels(IQMparameters(model))
+set(gca,'TickLabelInterpreter','none', 'fontsize',30)
+xtickangle(45)
+ylim([-1.05 1.05])
+box off
+ylabel({'Non-rejected parameter values,', 'relative to the threshold for non-identifiability'})
+hold on
+
+%% Plot the bounds relative to the non-identifiability threshold
+relativelb = 2*(lb-rejectlb) ./ (rejectub-rejectlb)-1;
+relativeub = 2*(ub-rejectlb) ./ (rejectub-rejectlb)-1;
+for i = 1:length(relativelb)
+ plot([i-0.3 i+0.3], [relativelb(i) relativelb(i)], 'color', [0.9290 0.6940 0.1250])
+ plot([i-0.3 i+0.3], [relativeub(i) relativeub(i)], 'color', [0.9290 0.6940 0.1250])
+end
+
+%% Plot the best solution
+load(FindBestParametersFile(baseFolder, 1, [modelName ', opt-eSS']), 'optParam')
+x = 2*(optParam-rejectlb) ./ (rejectub-rejectlb)-1;
+plot(x,'x','MarkerSize',15, 'color',[0 0.4470 0.7410]);
+
+%% Set size and save figure
+set(gcf, 'outerposition',[0 0 2560 1440], 'PaperType','a4')
+
+
+end
diff --git a/Scripts and data/PlotUncertainty.m b/Scripts and data/PlotUncertainty.m
index 4405a9a53d6b16177661c0040cefec2a1d8aedb4..3e5f0852820f96cd2724265f9f8f95248565dd42 100644
--- a/Scripts and data/PlotUncertainty.m
+++ b/Scripts and data/PlotUncertainty.m
@@ -1,144 +1,50 @@
-function [] = PlotUncertainty(doFig56, doFig4, res)
-close all
-%PLOTUNCERTAINTY Summary of this function goes here
-% Detailed explanation goes here
-if nargin<1, doFig56=0; end
-if nargin<2, doFig4=0; end
-if nargin<3, res=0.01; end
-if ~doFig56
- baseFolder = './Results, Fig2-3' ;
- load('./Results, Fig2-3/simple, opt-eSS(181.58).mat')
-elseif doFig56
- baseFolder = './Results, Fig5-6' ;
- load('./Results, Fig5-6/simple, opt-eSS(213.71).mat')
+function [] = PlotUncertainty(doDiabetes,res, modelName)
+clear mex
+
+if nargin<1, doDiabetes=0; end
+if nargin<2, res=0.01; end
+if nargin<3, modelName='lipolysis'; end
+
+if ~doDiabetes
+ baseFolder = './Parameter sets' ;
+elseif doDiabetes
+ baseFolder = './Parameter sets (with diabetes)' ;
end
-files=dir([baseFolder '/minmax/*.mat']);
-modelName='LipolysisModel';
-[model,data, lb, ub, nParams, expInd, stimulus, dgf] = Init(modelName, doFig56, 1);
+files=dir(sprintf('%s/PPL/*%s*.mat', baseFolder, modelName));
+
+[model,data, lb, ub, nParams, expInd, stimulus, dgf] = Init(modelName, doDiabetes, 0);
+limit = chi2inv(0.95,dgf);
stimulusHighRes=table();
ins=log10(unique([data.InVitro.FFA.Ins; data.InVitro.Glycerol.Ins]));
stimulusHighRes.Ins=[0 10.^(ins(2):res:ins(end)) 0]';
-stimulusHighRes.Iso=[10*ones(height(stimulusHighRes)-1,1); 0];
+stimulusHighRes.Iso=[0.01*ones(height(stimulusHighRes)-1,1); 0]; %10 nM = 0.01 µM
-if length(optParam)==length(lb)
- optParam=[optParam 1-eps];
-end
-bestParam=optParam;
-cost = costfunction(bestParam,model, expInd, data, stimulus, doFig56, 1);
-fprintf('Total cost: %.2f, chi2: %.2f. Pass: %d\n',cost, chi2inv(0.95,dgf), cost<chi2inv(0.95,dgf))
+load(FindBestParametersFile(baseFolder, 1, [modelName ', opt-eSS']), 'optParam')
-allParams=[];
-for i = fliplr(1:length(files))
- load([files(i).folder '/' files(i).name],'optParam');
- if length(optParam)==length(lb)
- optParam=[optParam 1-eps];
- end
- allParams(i,:)=optParam;
+if length(optParam)==length(IQMparameters(model))-5
+ optParam=[optParam 0];
end
-allParams=unique(allParams,'rows');
-if doFig4
- disp('Running batch 1/4')
-end
-[DR, DRHSL, DRDiabetes, TS] = SimulateAllParams(bestParam, allParams, model, data, expInd, stimulusHighRes, doFig56);
-
-if doFig4
- disp('Running batch 2/4')
- [DR_exclude1] = SimulateAllParams(bestParam, allParams, model, data, expInd, stimulusHighRes,doFig56, 'exclude1');
- disp('Running batch 3/4')
-
- [DR_exclude2] = SimulateAllParams(bestParam, allParams, model, data, expInd, stimulusHighRes,doFig56, 'exclude2');
- disp('Running batch 4/4')
- [~, ~, ~, TS_exclude3] = SimulateAllParams(bestParam, allParams, model, data, expInd, stimulusHighRes(1,:),doFig56, 'exclude3');
-end
+cost = costfunction(optParam,model, expInd, data, stimulus, doDiabetes);
+fprintf('Total cost: %.2f, chi2: %.2f. Pass: %d\n',cost, limit, cost<limit)
+params=[exp(optParam(1:expInd)) optParam(expInd+1:end)];
+diab=params(end);
+params(end)=[];
-%% Do the plotting
-%Setup simulations and data (in vitro)
-close all
-allInVitroData=struct();
-allInVitroData.Normal=data.InVitro;
-allInVitroData.Diabetes=data.InVitro_diabetes;
+Best=simulateInVitro(model, params, expInd, diab, stimulusHighRes, 0);
+InVitrotmp.high=Best.Normal;
+InVitrotmp.low=Best.Normal;
-% Plot estimation simulation and data
-if ~doFig4
- PlotInVivo(data, TS.Gly.Normal, 2,'Glycerol')
- PlotInVitro(DR, allInVitroData, {'Normal'}, 2)
-end
-if contains(baseFolder, 'Fig2-3') % Plot validation simulation and data
-
- if ~doFig4
- PlotInVitro(DRHSL, allInVitroData, {'Normal'}, 3)
- exportgraphics(figure(2),sprintf('./%s/Fig2-Estimation.pdf',baseFolder))
- exportgraphics(figure(3),sprintf('./%s/Fig3-Validation.pdf',baseFolder))
- elseif doFig4
- DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso', 'Glycerol'}))=[];
- PlotInVitro(DR, allInVitroData, {'Normal'}, 4,[], 1)
- PlotInVitro(DR_exclude1, allInVitroData, {'Normal'}, 4,[], 3)
- PlotInVitro(DR_exclude2, allInVitroData, {'Normal'}, 4,[], 5)
-
- PlotInVivo(data, TS.Gly.Normal, 4,'Glycerol', {'Fig3Epi','Fig3EpiPhe'}, 2)
- PlotInVivo(data, TS_exclude3.Gly.Normal, 4,'Glycerol', {'Fig3Epi','Fig3EpiPhe'}, 4)
- end
-
-
-elseif contains(baseFolder, 'Fig5-6') % Plot diabetes in vitro predictions.
- exportgraphics(figure(2),sprintf('./%s/FigS1-Estimation.pdf',baseFolder))
-
- DRDiabetes.Normal(:,~ismember(DRDiabetes.Normal.Properties.VariableNames,{'Ins','Iso','FFA','HSL'}))=[];
- DRDiabetes.Diabetes(:,~ismember(DRDiabetes.Diabetes.Properties.VariableNames,{'Ins','Iso','FFA', 'HSL'}))=[];
- DRDiabetes.Diabetes.HSL=nan(size(DRDiabetes.Diabetes.HSL));
- PlotInVitro(DRDiabetes, allInVitroData, {'Normal','Diabetes'}, 5)
- subplot(2,2,2)
- axsLine=findobj(gca,'type','line');
- legend([axsLine(8) axsLine(5)], {'non-diabetic','diabetic'}, 'location','best')
- exportgraphics(figure(5),sprintf('./%s/Fig5-Estimation-new.pdf',baseFolder))
-
- for i = 2:size(TS.FFA.Normal,2)
- maxPred=max(max([TS.FFA.Normal{:,i}; TS.FFA.Diabetes{:,i}]));
- TS.FFA.Normal{:,i}=TS.FFA.Normal{:,i}./maxPred*100;
- TS.FFA.Diabetes{:,i}=TS.FFA.Diabetes{:,i}./maxPred*100;
- end
- TS.FFA.Diabetes.Properties.VariableNames=strcat(TS.FFA.Diabetes.Properties.VariableNames,'Diab');
- TS.FFA=[TS.FFA.Normal, TS.FFA.Diabetes(:,2:end)];
- PlotInVivo(data.InVivo.Fig1.Time, TS.FFA, 6, 'FFA',{'Fig1', 'Fig2Epi', 'Fig1Diab', 'Fig2EpiDiab'})
- subplot(2,2,1)
- h=findobj(gca,'Type','line');
- h(7).YData=nan(size(h(7).YData));
- subplot(2,2,2)
- h=findobj(gca,'Type','line');
- h(7).YData=nan(size(h(7).YData));
- exportgraphics(figure(6),sprintf('./%s/Fig6-InVivo-FFA.pdf',baseFolder))
-
-end
-end
-
-function [DR, DRHSL, DRDiabetes, TS] = SimulateAllParams(optParam, allParams, model, data, expInd, stimulusHighRes, doDiabetes, exclude)
-if nargin < 8, exclude=''; end
-switch exclude
- case 'exclude1', allParams(:,28)=inf; optParam(28)=inf;
- case 'exclude2', allParams(:,29)=inf; optParam(29)=inf;
- case 'exclude3', allParams(:,27)=inf; optParam(27)=inf;
+if doDiabetes
+ InVitrotmpD.high=Best.Diabetes;
+ InVitrotmpD.low=Best.Diabetes;
end
-
-params=[exp(optParam(1:expInd)) optParam(expInd+1:end-1)];
-diab=optParam(end);
-
-[DRbest]=simulateDR(model, params, expInd, doDiabetes, diab, stimulusHighRes);
-DRtmp.high=DRbest.Normal;
-DRtmp.low=DRbest.Normal;
-if ~doDiabetes
- DRbest.Diabetes=DRbest.Normal;
-end
-
-DRbest.Diabetes{:,3:end}=nan;
-DRtmpD.high=DRbest.Diabetes;
-DRtmpD.low=DRbest.Diabetes;
-
-[TSBest] = SimulateInVivo(params, model, data.InVivo);
+TSBest = SimulateInVivo(params, model, data.InVivo, 0, 0);
+TSBest.FFA{:,2:end}=TSBest.FFA{:,2:end}./TSBest.FFA{1,2:end}; %ta bort?
TStmp.Gly.low=TSBest.Gly;
TStmp.Gly.high=TSBest.Gly;
TStmp.FFA.low=TSBest.FFA;
@@ -152,55 +58,144 @@ TStmpD.Gly.high=TSBestD.Gly;
TStmpD.FFA.low=TSBestD.FFA;
TStmpD.FFA.high=TSBestD.FFA;
-for i = 1:size(allParams,1)
- optParam=allParams(i,:);
-
- params=[exp(optParam(1:expInd)) optParam(expInd+1:end-1)];
- diab=optParam(end);
- Tmp=simulateDR(model, params, expInd, doDiabetes, diab, stimulusHighRes);
- DRtmp.low{:,:}=min(DRtmp.low{:,:}, Tmp.Normal{:,:});
- DRtmp.high{:,:}=max(DRtmp.high{:,:}, Tmp.Normal{:,:});
- if doDiabetes
- DRtmpD.low{:,:}=min(DRtmpD.low{:,:}, Tmp.Diabetes{:,:});
- DRtmpD.high{:,:}=max(DRtmpD.high{:,:}, Tmp.Diabetes{:,:});
+optParams=[];
+for i = fliplr(1:length(files))
+ load([files(i).folder '/' files(i).name],'optParam');
+ if length(optParam)==length(lb)
+ optParam=[optParam 0];
+ end
+ optParams(i,:)=optParam;
+end
+optParams=unique(optParams,'rows');
+if size(optParams,1)>0
+ disp('Simulating parameter sets')
+end
+for i = 1:size(optParams,1)
+ optParam=optParams(i,:);
+ cost = costfunction(optParam,model, expInd, data, stimulus, doDiabetes);
+ if cost<limit+0.1
+ params=[exp(optParam(1:expInd)) optParam(expInd+1:end-1)];
+ diab=optParam(end);
+
+ [Tmp]=simulateInVitro(model, params, expInd, diab, stimulusHighRes);
+ InVitrotmp.low{:,:}=min(InVitrotmp.low{:,:}, Tmp.Normal{:,:});
+ InVitrotmp.high{:,:}=max(InVitrotmp.high{:,:}, Tmp.Normal{:,:});
- TmpD = SimulateInVivo(params, model, data.InVivo, diab);
- TStmpD.Gly.low{:,:}=min(TStmpD.Gly.low{:,:}, TmpD.Gly{:,:});
- TStmpD.Gly.high{:,:}=max(TStmpD.Gly.high{:,:}, TmpD.Gly{:,:});
- TStmpD.FFA.low{:,:}=min(TStmpD.FFA.low{:,:}, TmpD.FFA{:,:});
- TStmpD.FFA.high{:,:}=max(TStmpD.FFA.high{:,:}, TmpD.FFA{:,:});
+ TmpTS = SimulateInVivo(params, model, data.InVivo, 0, 0);
+ if isfield(Tmp,'Diabetes')
+ InVitrotmpD.low{:,:}=min(InVitrotmpD.low{:,:}, Tmp.Diabetes{:,:});
+ InVitrotmpD.high{:,:}=max(InVitrotmpD.high{:,:}, Tmp.Diabetes{:,:});
+
+ TmpTSD = SimulateInVivo(params, model, data.InVivo, diab, 0);
+ TmpTSD.FFA{:,2:end}=TmpTSD.FFA{:,2:end}./TmpTS.FFA{1,2:end};
+
+ TStmpD.Gly.low{:,:}=min(TStmpD.Gly.low{:,:}, TmpTSD.Gly{:,:});
+ TStmpD.Gly.high{:,:}=max(TStmpD.Gly.high{:,:}, TmpTSD.Gly{:,:});
+ TStmpD.FFA.low{:,:}=min(TStmpD.FFA.low{:,:}, TmpTSD.FFA{:,:});
+ TStmpD.FFA.high{:,:}=max(TStmpD.FFA.high{:,:}, TmpTSD.FFA{:,:});
+ end
+ TmpTS.FFA{:,2:end}=TmpTS.FFA{:,2:end}./TmpTS.FFA{1,2:end};
+ TStmp.Gly.low{:,:}=min(TStmp.Gly.low{:,:}, TmpTS.Gly{:,:});
+ TStmp.Gly.high{:,:}=max(TStmp.Gly.high{:,:}, TmpTS.Gly{:,:});
+ TStmp.FFA.low{:,:}=min(TStmp.FFA.low{:,:}, TmpTS.FFA{:,:});
+ TStmp.FFA.high{:,:}=max(TStmp.FFA.high{:,:}, TmpTS.FFA{:,:});
+
+ else
+ disp("Not a valid solution")
+ end
+ if i==1
+ fprintf('%i of %i \n|',i,size(optParams,1))
+ elseif mod(i,25)==0
+ fprintf(' %i of %i \n',i,size(optParams,1))
+ else
+ fprintf('|')
end
-
- Tmp = SimulateInVivo(params, model, data.InVivo);
- TStmp.Gly.low{:,:}=min(TStmp.Gly.low{:,:}, Tmp.Gly{:,:});
- TStmp.Gly.high{:,:}=max(TStmp.Gly.high{:,:}, Tmp.Gly{:,:});
- TStmp.FFA.low{:,:}=min(TStmp.FFA.low{:,:}, Tmp.FFA{:,:});
- TStmp.FFA.high{:,:}=max(TStmp.FFA.high{:,:}, Tmp.FFA{:,:});
-
- fprintf('%i of %i \n',i,size(allParams,1))
end
+%% Do the plotting
+%Setup simulations and data (in vitro)
+allInVitroData=struct();
+allInVitroData.Normal=data.InVitro;
-DR.Normal=ConcatenateTableColums(DRbest.Normal, DRtmp.low(:,3:end));
-DR.Normal=ConcatenateTableColums(DR.Normal, DRtmp.high(:,3:end));
-DR.Diabetes=ConcatenateTableColums(DRbest.Diabetes, DRtmpD.low(:,3:end));
-DR.Diabetes=ConcatenateTableColums(DR.Diabetes, DRtmpD.high(:,3:end));
-DRHSL=DR;
-DRHSL.Normal(:,~ismember(DRHSL.Normal.Properties.VariableNames,{'Ins','Iso','HSL'}))=[];
-DRDiabetes=DR;
+InVitro.Normal=ConcatenateTableColums(Best.Normal, InVitrotmp.low(:,3:end));
+InVitro.Normal=ConcatenateTableColums(InVitro.Normal, InVitrotmp.high(:,3:end));
+InVitroHSL=InVitro;
+
+if doDiabetes
+ allInVitroData.Diabetes=data.InVitro_diabetes;
+
+ InVitro.Diabetes=ConcatenateTableColums(Best.Diabetes, InVitrotmpD.low(:,3:end));
+ InVitro.Diabetes=ConcatenateTableColums(InVitro.Diabetes, InVitrotmpD.high(:,3:end));
+ InVitroDiabetes=InVitro;
+
+ TS.FFA.Diabetes=ConcatenateTableColums(TSBestD.FFA, TStmpD.FFA.low(:,2:end));
+ TS.FFA.Diabetes=ConcatenateTableColums(TS.FFA.Diabetes, TStmpD.FFA.high(:,2:end));
+end
%Setup simulations and data (in vivo)
TS.Gly.Normal=ConcatenateTableColums(TSBest.Gly, TStmp.Gly.low(:,2:end));
TS.Gly.Normal=ConcatenateTableColums(TS.Gly.Normal, TStmp.Gly.high(:,2:end));
-if isempty(exclude)
- TS.FFA.Normal=ConcatenateTableColums(TSBest.FFA, TStmp.FFA.low(:,2:end));
- TS.FFA.Normal=ConcatenateTableColums(TS.FFA.Normal, TStmp.FFA.high(:,2:end));
-
- TS.FFA.Diabetes=ConcatenateTableColums(TSBestD.FFA, TStmpD.FFA.low(:,2:end));
- TS.FFA.Diabetes=ConcatenateTableColums(TS.FFA.Diabetes, TStmpD.FFA.high(:,2:end));
- DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso','FFA', 'Glycerol','PKB'}))=[];
-else
- DR.Normal(:,~ismember(DR.Normal.Properties.VariableNames,{'Ins','Iso', 'Glycerol'}))=[];
+TS.FFA.Normal=ConcatenateTableColums(TSBest.FFA, TStmp.FFA.low(:,2:end));
+TS.FFA.Normal=ConcatenateTableColums(TS.FFA.Normal, TStmp.FFA.high(:,2:end));
+
+InVitro.Normal(:,~ismember(InVitro.Normal.Properties.VariableNames,{'Ins','Iso','FFA', 'Glycerol','PKB'}))=[];
+Best.Normal(:,~ismember(Best.Normal.Properties.VariableNames,{'Ins','Iso','FFA', 'Glycerol','PKB'}))=[];
+
+%% Plot
+if contains(modelName,'_noIns')
+ PlotInVivo(data, TS.Gly.Normal, 52,'Glycerol')
+ PlotInVitro(InVitro, allInVitroData, {'Normal'}, 52)
+elseif ~contains(baseFolder, 'with diabetes') % Plot validation simulation and data
+
+ PlotInVivo(data, TS.Gly.Normal, 51,'Glycerol')
+ PlotInVitro(InVitro, allInVitroData, {'Normal'}, 51)
+
+ data.InVivo.Fig3Epi=[];
+ TS.Gly.Normal.Fig3Epi=[];
+ PlotInVivo(data, TS.Gly.Normal, 3,'Glycerol')
+ PlotInVitro(InVitro, allInVitroData, {'Normal'}, 3)
+
+ InVitroHSL.Normal(:,~ismember(InVitroHSL.Normal.Properties.VariableNames,{'Ins','Iso','HSL'}))=[];
+ PlotInVitro(InVitroHSL, allInVitroData, {'Normal'}, 4)
+elseif contains(baseFolder, 'with diabetes') % Plot diabetes in vitro predictions.
+
+ PlotInVivo(data, TS.Gly.Normal, 53,'Glycerol')
+ PlotInVitro(InVitro, allInVitroData, {'Normal'}, 53)
+
+ data.InVivo.Fig3Epi=[];
+ TS.Gly.Normal.Fig3Epi=[];
+ PlotInVivo(data, TS.Gly.Normal, 54,'Glycerol')
+ PlotInVitro(InVitro, allInVitroData, {'Normal'}, 54)
+
+
+ InVitroDiabetes.Normal(:,~ismember(InVitroDiabetes.Normal.Properties.VariableNames,{'Ins','Iso','FFA', 'Glycerol', 'HSL', 'Reesterification'}))=[];
+ InVitroDiabetes.Diabetes(:,~ismember(InVitroDiabetes.Diabetes.Properties.VariableNames,{'Ins','Iso','FFA', 'Glycerol', 'HSL', 'Reesterification'}))=[];
+
+ allInVitroData.Normal.cAMP=[];
+ PlotInVitro(InVitroDiabetes, allInVitroData, {'Normal','Diabetes'}, 6)
+
+ %%
+ if height(stimulusHighRes)<800
+ fprintf('\n\nNote: running with a low resolution might yield slightly incorrect bounds for the reesterification\n')
+ end
+ reestMax = max(max(InVitroDiabetes.Normal.Reesterification(1:end-1,:)));
+ reestMin = min(min(InVitroDiabetes.Normal.Reesterification(1:end-1,:)));
+ fprintf('Normal reesterification (iso+ins stimulated) interval: %.2f - %.2f\n',reestMin, reestMax)
+
+ reestDiabMax = max(max(InVitroDiabetes.Diabetes.Reesterification(1:end-1,:)));
+ reestDiabMin = min(min(InVitroDiabetes.Diabetes.Reesterification(1:end-1,:)));
+ fprintf('Diabetic reesterification (iso+ins stimulated) interval: %.2f - %.2f\n\n', reestDiabMin, reestDiabMax)
+
+ %%
+ TS.FFA.Diabetes.Properties.VariableNames=strcat(TS.FFA.Diabetes.Properties.VariableNames,'Diab');
+ TS.FFA=[TS.FFA.Normal, TS.FFA.Diabetes(:,2:end)];
+ PlotInVivo(data.InVivo.Fig1.Time, TS.FFA, 7, 'FFA',{'Fig1', 'Fig2Epi', 'Fig1Diab', 'Fig2EpiDiab'})
+ subplot(2,2,1)
+ ylabel({'Released FA', 'fold over non-diabetic basal'})
+ subplot(2,2,2)
+ ylabel({'Released FA', 'fold over non-diabetic basal'})
+
end
-end
\ No newline at end of file
+end
+
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diff --git a/Scripts and data/Setup.m b/Scripts and data/Setup.m
index a6a9adc5c6d6fa440594e0a2a0779751c1c57205..b3b76873a00dca8cce3e78fd217ddd1bded869ab 100644
--- a/Scripts and data/Setup.m
+++ b/Scripts and data/Setup.m
@@ -1,10 +1,17 @@
function []=Setup()
if ~exist('IQMsimulate','file')
- choice = input('For these scripts to work, the IQM tools toolbox have to be compiled. \nThis needs a valid C-compiler.\nPress enter to continue.');
- run('./IQM Tools/installIQMtoolsInitial.m')
+ fprintf('\n\nFor these scripts to work, the IQM tools toolbox have to be compiled.\n')
+ disp('To do this, a valid C-compiler is necessary')
+ disp('If the compilation of the toolbox does not, it is likely that a valid C-compiler is missing.')
+ choice = input('press enter to continue and compile the toolbox.');
+ run('./IQM Tools/installIQMtoolsInitial.m')
end
%% Compile models
-
+disp("Compiling models. Please wait.")
+files = dir('*.txt');
+for i = 1:length(files)
+ IQMmakeMEXmodel(IQMmodel([files(i).folder '/' files(i).name]))
+end
addpath(genpath('.'));
end
diff --git a/Scripts and data/SimulateInVivo.m b/Scripts and data/SimulateInVivo.m
index 9cb473f49a4a85328d5275293cf3cbcae1c94423..15ce665a53217d64b0d729b475091f016156512e 100644
--- a/Scripts and data/SimulateInVivo.m
+++ b/Scripts and data/SimulateInVivo.m
@@ -1,20 +1,27 @@
-function [responses, scale, responsesRaw]=SimulateInVivo(params, model, data, diab)
+function [responses, penalty, scale, responsesRaw]=SimulateInVivo(params, model, data, diab, tol)
ins=data.Fig1.Ins;
-if nargin <4, diab= 1; end %not used in most simulations. 1 = no diabition
+if nargin<4, diab= 0; end %not used in most simulations. 0 = no diabition
+if nargin<5, tol =0;end
simOptions = [];
simOptions.method = 'stiff';
simOptions.maxnumsteps = 1e4;
try
- init = model([0 20000-10 20000],[],[params diab 0 0 0 ins],simOptions);
+ init = model([0 30000-10 30000],[],[params diab 0 0e-4 0 ins],simOptions);
+
+ steady = abs(init.statevalues(end-1,:)-init.statevalues(end,:));
+ steady = sum(steady(steady>1e-6))*1e9;
+
catch err
cost = 1e90;
responsesRaw=[];
responses=[];
+ steady=1e20;
return
end
+
ic = init.statevalues(end,:);
responsesRaw.Gly=table();
responsesRaw.Gly.Time=(data.Fig1.Time(1):data.Fig1.Time(end))';
@@ -23,45 +30,61 @@ responsesRaw.FFA.Time=(data.Fig1.Time(1):data.Fig1.Time(end))';
ind = ismember(data.Fig1.Time(1):data.Fig1.Time(end), data.Fig1.Time);
-Fig1 = SimulateExperiment(params, diab, model, data.Fig1, ic, [1e3 0 0]);
-yh=Fig1(ind, 1);
+[Epi, simEpi] = SimulateExperiment(params, diab, model, data.Fig1, ic, [1 0 0]);
+yh=Epi(ind, 1);
+yh = [ones(size(yh)) yh];
y=data.Fig1.Mean;
SEM=data.Fig1.SEM;
scale1=lscov(yh,y, SEM.^-2);
-responsesRaw.Gly.Fig1=Fig1(:,1);
-responsesRaw.FFA.Fig1=Fig1(:,2);
-
-Fig2Epi = SimulateExperiment(params, diab, model, data.Fig2Epi, ic, [1e3 0 0]);
-Fig2Iso = SimulateExperiment(params, diab, model, data.Fig2Iso, ic, [0 1e2 0]);
-yh=[Fig2Epi(ind, 1); Fig2Iso(ind, 1)];
+responsesRaw.Gly.Fig1=Epi(:,1);
+responsesRaw.FFA.Fig1=Epi(:,2);
+penaltyEpi = Penalty(simEpi.statevalues, tol);
+
+[EpiIns, simEpiIns] = SimulateExperiment(params, diab, model, data.Fig2Epi, ic, [1 0 0]);
+[IsoIns, simIsoIns] = SimulateExperiment(params, diab, model, data.Fig2Iso, ic, [0e-4 0.1 0]);
+yh=[EpiIns(ind, 1); IsoIns(ind, 1)];
+yh = [ones(size(yh)) yh];
y=[data.Fig2Epi.Mean; data.Fig2Iso.Mean];
SEM=[data.Fig2Epi.SEM; data.Fig2Iso.SEM];
scale2=lscov(yh,y, SEM.^-2);
-responsesRaw.Gly.Fig2Epi=Fig2Epi(:,1);
-responsesRaw.Gly.Fig2Iso=Fig2Iso(:,1);
-responsesRaw.FFA.Fig2Epi=Fig2Epi(:,2);
-responsesRaw.FFA.Fig2Iso=Fig2Iso(:,2);
-
-Fig3Epi = SimulateExperiment(params, diab, model, data.Fig3Epi, ic, [1e3 0 0]);
-Fig3EpiPhe = SimulateExperiment(params, diab, model, data.Fig3EpiPhe, ic, [1e3 0 1]);
-yh=[Fig3Epi(ind, 1); Fig3EpiPhe(ind, 1)];
+responsesRaw.Gly.Fig2Epi=EpiIns(:,1);
+responsesRaw.Gly.Fig2Iso=IsoIns(:,1);
+responsesRaw.FFA.Fig2Epi=EpiIns(:,2);
+responsesRaw.FFA.Fig2Iso=IsoIns(:,2);
+penaltyEpiIns = Penalty(simEpiIns.statevalues, tol); %3-4 is equal to the alpha receptor, which does not change if no epi is present
+penaltyIsoIns = Penalty(simIsoIns.statevalues(:,[1:2 5:end]), tol);
+
+[EpiInsPhe, simEpiPhe] = SimulateExperiment(params, diab, model, data.Fig3EpiPhe, ic, [1 0 1]);
+yh=[EpiIns(ind, 1); EpiInsPhe(ind, 1)];
+yh = [ones(size(yh)) yh];
y=[data.Fig3Epi.Mean; data.Fig3EpiPhe.Mean];
SEM=[data.Fig3Epi.SEM; data.Fig3EpiPhe.SEM];
scale3=lscov(yh,y, SEM.^-2);
-responsesRaw.Gly.Fig3Epi=Fig3Epi(:,1);
-responsesRaw.Gly.Fig3EpiPhe=Fig3EpiPhe(:,1);
-responsesRaw.FFA.Fig3Epi=Fig3Epi(:,2);
-responsesRaw.FFA.Fig3EpiPhe=Fig3EpiPhe(:,2);
-
+responsesRaw.Gly.Fig3Epi=EpiIns(:,1);
+responsesRaw.Gly.Fig3EpiPhe=EpiInsPhe(:,1);
+responsesRaw.FFA.Fig3Epi=EpiIns(:,2);
+responsesRaw.FFA.Fig3EpiPhe=EpiInsPhe(:,2);
+penaltyEpiInsPhe = Penalty(simEpiPhe.statevalues, tol);
scale=[scale1 scale2 scale2 scale3 scale3];
responses=responsesRaw;
-responses.Gly{:,2:end}=responses.Gly{:,2:end}.*scale;
-responses.FFA{:,2:end}=responses.FFA{:,2:end}.*scale;
+responses.Gly{:,2:end}=responses.Gly{:,2:end}.*scale(2,:) + scale(1,:);
+responses.FFA{:,2:end}=responses.FFA{:,2:end}.*scale(2,1) + scale(1,1);
+penalty = steady+penaltyEpi+penaltyEpiIns+penaltyIsoIns+penaltyEpiInsPhe; % Force the simulations to have reasonable (unscaled) values.
end
-function [response] = SimulateExperiment(params, diab, model, data, ic, stimulus)
+function [penalty] = Penalty(sim, tol)
+if nargin <2, tol = 0; end
+if any(max(sim)-min(sim) <= 0.1-tol)
+ penalties = 1./(max(sim)-min(sim));
+ penalty = sum(penalties(isfinite(penalties)))+100;
+else
+ penalty = 0;
+end
+end
+
+function [response, sim] = SimulateExperiment(params, diab, model, data, ic, stimulus)
simOptions = [];
simOptions.method = 'stiff';
simOptions.maxnumsteps = 1e4;
@@ -69,8 +92,8 @@ simOptions.maxnumsteps = 1e4;
Adr = stimulus(1);
Iso = stimulus(2);
Phe = stimulus(3);
-if diab~=1 %increase basal levels of insulin in diabetes
- data.Ins(data.Ins==min(data.Ins)) = min(data.Ins)*2;
+if diab~=0 %increase basal levels of insulin in diabetes
+ data.Ins(data.Ins==min(data.Ins)) = min(data.Ins)*2;
end
Ins = min(data.Ins);
@@ -78,20 +101,16 @@ t = data.Time;
ind=contains(IQMvariables(model),{'Gly','FFA'});
try
- init = model([0 2000-10 2000],ic,[params diab 0 0 0 Ins],simOptions);
-
- ic = init.statevalues(end,:);
-
- simBasal = model(t(1):t(2)+5,ic,[params diab 0 0 0 Ins],simOptions); %Steady state
+ simBasal = model(t(1):t(2)+5,ic,[params diab 0 0e-4 0 Ins],simOptions); %Steady state
simLow = model(t(2)+5:t(5)+5,simBasal.statevalues(end,:),[params diab Phe Adr Iso Ins],simOptions); % low dose
simHigh = model(t(5)+5:t(8)+5,simLow.statevalues(end,:),[params diab Phe Adr*10 Iso*10 Ins],simOptions); % high dose
Ins = max(data.Ins); %start infusion of insulin
- simIns = model(t(8)+5:t(16)+5,simHigh.statevalues(end,:),[params diab 0 0 0 Ins],simOptions); % insulin only
+ simIns = model(t(8)+5:t(16)+5,simHigh.statevalues(end,:),[params diab 0 0e-4 0 Ins],simOptions); % insulin only
simInsLow = model(t(16)+5:t(19)+5,simIns.statevalues(end,:),[params diab Phe Adr Iso Ins],simOptions); % low dose
simInsHigh = model(t(19)+5:t(22)+5,simInsLow.statevalues(end,:),[params diab Phe Adr*10 Iso*10 Ins],simOptions); %high dose
- simIns2 = model(t(22)+5:t(30),simInsHigh.statevalues(end,:),[params diab 0 0 0 Ins],simOptions); %insulin only
-
+ simIns2 = model(t(22)+5:t(30),simInsHigh.statevalues(end,:),[params diab 0 0e-4 0 Ins],simOptions); %insulin only
+
response = [simBasal.variablevalues(:,ind);
simLow.variablevalues(2:end,ind);
simHigh.variablevalues(2:end,ind);
@@ -99,10 +118,10 @@ try
simInsLow.variablevalues(2:end,ind);
simInsHigh.variablevalues(2:end,ind);
simIns2.variablevalues(2:end,ind)];
-
+ sim = StructsFieldCat([simBasal;simLow;simHigh;simIns;simInsLow;simInsHigh;simIns2]); %combines multiple structs to one.
catch err
- response= repmat(1e90,fliplr(size(t(1):t(end))));
+ response= repmat(1e90,fliplr(size(t(1):0.01:t(end))));
return
end
diff --git a/Scripts and data/StructFieldCat.m b/Scripts and data/StructFieldCat.m
index a3039f571af9bd7eea52ed9b5c5240c6617f1f51..9657ebcb682f5f440dabc78741da6fddbd4f3436 100644
--- a/Scripts and data/StructFieldCat.m
+++ b/Scripts and data/StructFieldCat.m
@@ -5,7 +5,13 @@ end
for i=1:size(T,2)
fields = fieldnames(S);
for k = 1:numel(fields)
- aField = fields{k};
- S.(aField) = cat(dim, S.(aField), T(i).(aField));
+ aField = fields{k}; % EDIT: changed to {}
+ if size(S.(aField),1) ==1 && size(S.(aField),2)>1 % If field is a horizontal 1D array
+ S.(aField) = horzcat(S.(aField), T(i).(aField));
+ elseif size(S.(aField),2) > 1 && size(S.(aField),1)>1 %if field is a vertical 1D array
+ S.(aField) = vertcat(S.(aField), T(i).(aField));
+ else
+ S.(aField) = cat(dim, S.(aField), T(i).(aField));
+ end
end
end
\ No newline at end of file
diff --git a/Scripts and data/StructsFieldCat.m b/Scripts and data/StructsFieldCat.m
new file mode 100644
index 0000000000000000000000000000000000000000..69561b8d356fbd7683472e4bdace354bf9f50e34
--- /dev/null
+++ b/Scripts and data/StructsFieldCat.m
@@ -0,0 +1,12 @@
+function [s] = StructsFieldCat(structs, dim)
+%STRUCTSFIELDCAT Summary of this function goes here
+% Detailed explanation goes here
+if nargin<2 || isempty(dim)
+ dim=1;
+end
+s = structs(1);
+for i = 2:length(structs)
+ s=StructFieldCat(s, structs(i), dim);
+end
+end
+
diff --git a/Scripts and data/costfunction.m b/Scripts and data/costfunction.m
index c90d9a8a2042486c7d5fbdd4bce70aef51484258..dca1feb5d36ef28a7f7f896a7d1552b7ecb70c1f 100644
--- a/Scripts and data/costfunction.m
+++ b/Scripts and data/costfunction.m
@@ -1,24 +1,20 @@
-function [cost] = costfunction(param_in,model, expInd, data, stimulus, doDiabetes, doPlot)
+function [cost] = costfunction(param_in,model, expInd, data, stimulus, doDiabetes)
if (nargin < 6), doDiabetes = 0; end
-if (nargin < 7), doPlot = 0; end
if iscolumn(param_in); param_in=param_in'; end
params=[exp(param_in(1:expInd)) param_in(expInd+1:end)];
-if length(param_in)==33 % hardcorded for LipolysisModel model, to account for the fact that some parameter sets where collected without diabetes as a free parameter. TODO: Extend previously collected parameter sets
+if length(param_in)+4==length(IQMparameters(model))
diabetes=params(end);
params(end)=[];
else
diabetes=0.66; % not used
end
-costInVitro = CostInVivo(params, model, data, doPlot);
-costTot = CostDR(model, params, expInd, stimulus, data,diabetes, doDiabetes, doPlot);
+costInVivo = CostInVivo(params, model, data.InVivo, 0);%% InVivo data
+costInVitro = CostInVitro(model, params, expInd, stimulus, data,diabetes, doDiabetes);%% InVitro data
-cost=costInVitro+costTot;
+cost=costInVivo+costInVitro;
end
-
-
-
diff --git a/Scripts and data/lipolysis.txt b/Scripts and data/lipolysis.txt
new file mode 100644
index 0000000000000000000000000000000000000000..162920d75017c3ad96db94c5b4f94b3c31082d15
--- /dev/null
+++ b/Scripts and data/lipolysis.txt
@@ -0,0 +1,120 @@
+********** MODEL NAME
+lipolysis
+********** MODEL NOTES
+
+********** MODEL STATES
+d/dt(BETA) = -v4a + v4b
+d/dt(BETAa) = v4a - v4b
+d/dt(ALPHA) = -v3a + v3b
+d/dt(ALPHAa) = v3a - v3b
+d/dt(AC) = -v5a + v5b
+d/dt(ACa) = v5a - v5b
+d/dt(PKB) = -v1a + v1b
+d/dt(PKBp) = v1a - v1b
+d/dt(PDE3B) = -v2a + v2b
+d/dt(PDE3Ba) = v2a - v2b
+d/dt(cAMP) = v6a - v6b
+d/dt(HSL) = -v7a + v7b
+d/dt(HSLp) = v7a - v7b
+d/dt(Gly) = v8a - v8b
+d/dt(FFA) = 3*v8a - v8c
+
+BETA(0) = 80
+BETAa(0) = 20
+ALPHA(0) = 80
+ALPHAa(0) = 20
+AC(0) = 80
+ACa(0) = 20
+PKB(0) = 80
+PKBp(0) = 20
+PDE3B(0) = 80
+PDE3Ba(0) = 20
+cAMP(0) = 0
+HSL(0) = 80
+HSLp(0) = 20
+Gly(0) = 0
+FFA(0)= 0
+********** MODEL PARAMETERS
+kdrift=0
+k4a = 62
+k4a2 = 0
+k4b = 0.12
+
+k3b = 1e-5
+k3a = 0.0004
+k3a2 = 1
+k5a = 0.0025
+k5b = 4.6
+
+k1a=1
+k1a2=1
+k1b=1
+
+k2a = 2.1
+k2b = 7.3
+k6a = 0.2
+k6b = 0.05
+k7a = 0.3
+k7b = 0.07
+k8a = 20
+k8c = 1 % reesterification parameter
+k8b = 1e-3
+
+phe_effect = 1
+
+isoscale = 10
+
+min3=0
+min1=0
+min2=0
+
+EC503=4
+EC501=7
+EC502=1
+n3=8
+n1=8
+n2=1
+
+diab = 0
+
+phe = 0
+adr = 0
+iso = 0
+insulin = 37.2
+
+
+********** MODEL VARIABLES
+y_PKB=PKBp
+y_HSL=HSLp
+y_Glycerol=Gly-kdrift*time
+y_FFA=FFA-kdrift*time
+
+Ins_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3);
+Ins_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
+Ins_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2);
+
+********** MODEL REACTIONS
+v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
+v4b = k4b * BETAa
+v3a = (k3a*Ins_3*adr+k3a2)*(1-phe_effect*phe)*ALPHA
+v3b = k3b * ALPHAa
+
+v1a = (k1a*cAMP+k1a2*Ins_1)*PKB
+v1b = k1b*PKBp
+v2a = k2a*PKBp*PDE3B
+v2b = k2b * PDE3Ba*Ins_2
+v5a = k5a * BETAa * AC
+v5b = k5b * ALPHAa * ACa
+v6a = k6a*ACa
+v6b = k6b*PDE3Ba*cAMP
+v7a = k7a*cAMP*HSL
+v7b = k7b * HSLp
+v8a = k8a * HSLp
+v8b = k8b * Gly
+v8c = (k8b+k8c*(1-diab)) * FFA
+********** MODEL FUNCTIONS
+
+********** MODEL EVENTS
+
+********** MODEL MATLAB FUNCTIONS
+
diff --git a/Scripts and data/lipolysis_noIns1.txt b/Scripts and data/lipolysis_noIns1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9f7841d09cbaadaf137579ac7877c6e50dd5b50
--- /dev/null
+++ b/Scripts and data/lipolysis_noIns1.txt
@@ -0,0 +1,118 @@
+********** MODEL NAME
+lipolysis_noIns1
+********** MODEL NOTES
+
+********** MODEL STATES
+d/dt(BETA) = -v4a + v4b
+d/dt(BETAa) = v4a - v4b
+d/dt(ALPHA) = -v3a + v3b
+d/dt(ALPHAa) = v3a - v3b
+d/dt(AC) = -v5a + v5b
+d/dt(ACa) = v5a - v5b
+d/dt(PKB) = -v1a + v1b
+d/dt(PKBp) = v1a - v1b
+d/dt(PDE3B) = -v2a + v2b
+d/dt(PDE3Ba) = v2a - v2b
+d/dt(cAMP) = v6a - v6b
+d/dt(HSL) = -v7a + v7b
+d/dt(HSLp) = v7a - v7b
+d/dt(Gly) = v8a - v8b
+d/dt(FFA) = 3*v8a - v8c
+
+BETA(0) = 80
+BETAa(0) = 20
+ALPHA(0) = 80
+ALPHAa(0) = 20
+AC(0) = 80
+ACa(0) = 20
+PKB(0) = 80
+PKBp(0) = 20
+PDE3B(0) = 80
+PDE3Ba(0) = 20
+cAMP(0) = 0
+HSL(0) = 80
+HSLp(0) = 20
+Gly(0) = 0
+FFA(0)= 0
+********** MODEL PARAMETERS
+kdrift=0
+k4a = 62
+k4a2 = 0
+k4b = 0.12
+
+k3b = 1e-5
+k3a = 0.0004
+k3a2 = 1
+k5a = 0.0025
+k5b = 4.6
+
+k1a=1
+k1a2=1
+k1b=1
+
+k2a = 2.1
+k2b = 7.3
+k6a = 0.2
+k6b = 0.05
+k7a = 0.3
+k7b = 0.07
+k8a = 20
+k8c = 1 % reesterification parameter
+k8b = 1e-3
+
+phe_effect = 1
+
+isoscale = 10
+
+min3=0
+min1=0
+min2=0
+
+EC503=4
+EC502=1
+n3=8
+n2=1
+
+diab = 0
+
+phe = 0
+adr = 0
+iso = 0
+insulin = 37.2
+
+
+********** MODEL VARIABLES
+y_PKB=PKBp
+y_Glycerol=Gly-kdrift*time
+y_HSL=HSLp
+y_FFA=FFA-kdrift*time
+
+IR_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3)
+IR_1 = min1
+IR_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2)
+
+********** MODEL REACTIONS
+v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
+v4b = k4b * BETAa
+v3a = (k3a*IR_3*adr+k3a2)*(1-phe_effect*phe)*ALPHA
+v3b = k3b * ALPHAa
+
+v1a = (k1a*cAMP+k1a2*IR_1)*PKB
+v1b = k1b*PKBp
+v2a = k2a*PKBp*PDE3B
+v2b = k2b * PDE3Ba*IR_2
+v5a = k5a * (BETAa) * AC
+v5b = (k5b * ALPHAa) * ACa
+v6a = k6a*ACa
+v6b = (k6b*PDE3Ba)*cAMP
+v7a = k7a*cAMP*HSL
+v7b = k7b * HSLp
+v8a = k8a * HSLp
+v8b = k8b * Gly
+v8c = (k8b+k8c*(1-diab)) * FFA
+********** MODEL FUNCTIONS
+
+********** MODEL EVENTS
+
+********** MODEL MATLAB FUNCTIONS
+
diff --git a/Scripts and data/LipolysisModel.txt b/Scripts and data/lipolysis_noIns2.txt
similarity index 74%
rename from Scripts and data/LipolysisModel.txt
rename to Scripts and data/lipolysis_noIns2.txt
index daf1a02621cb6454da57c72df1afcd81bb7c30fa..66110e4865caf54ef92613e7e069796c62e16dc0 100644
--- a/Scripts and data/LipolysisModel.txt
+++ b/Scripts and data/lipolysis_noIns2.txt
@@ -1,8 +1,6 @@
********** MODEL NAME
-LipolysisModel
-
+lipolysis_noIns2
********** MODEL NOTES
-A version of the pool model, without the pool.
********** MODEL STATES
d/dt(BETA) = -v4a + v4b
@@ -22,19 +20,19 @@ d/dt(Gly) = v8a - v8b
d/dt(FFA) = 3*v8a - v8c
BETA(0) = 80
-BETAa(0) = 5
+BETAa(0) = 20
ALPHA(0) = 80
ALPHAa(0) = 20
-AC(0) = 75
-ACa(0) = 25
-PKB(0) = 20
-PKBp(0) = 80
-PDE3B(0) = 20
-PDE3Ba(0) = 80
-cAMP(0) = 1
-HSL(0) = 85
-HSLp(0) = 15
-Gly(0) = 220
+AC(0) = 80
+ACa(0) = 20
+PKB(0) = 80
+PKBp(0) = 20
+PDE3B(0) = 80
+PDE3Ba(0) = 20
+cAMP(0) = 0
+HSL(0) = 80
+HSLp(0) = 20
+Gly(0) = 0
FFA(0)= 0
********** MODEL PARAMETERS
kdrift=0
@@ -44,6 +42,7 @@ k4b = 0.12
k3b = 1e-5
k3a = 0.0004
+k3a2 = 1
k5a = 0.0025
k5b = 4.6
@@ -53,15 +52,16 @@ k1b=1
k2a = 2.1
k2b = 7.3
-k6basal = 3.7
k6a = 0.2
k6b = 0.05
-k6b2 = 0.02
k7a = 0.3
k7b = 0.07
k8a = 20
k8c = 1 % reesterification parameter
k8b = 1e-3
+
+phe_effect = 1
+
isoscale = 10
min3=0
@@ -70,12 +70,10 @@ min2=0
EC503=4
EC501=7
-EC502=1
n3=8
n1=8
-n2=1
-diab = 1
+diab = 0
phe = 0
adr = 0
@@ -84,36 +82,34 @@ insulin = 37.2
********** MODEL VARIABLES
-y_alpha=ALPHAa
-y_PDE3Ba=PDE3Ba
y_PKB=PKBp
-y_cAMP=cAMP
y_Glycerol=Gly-kdrift*time
y_HSL=HSLp
y_FFA=FFA-kdrift*time
IR_3 = 100+(min3-100)/(1+(insulin/(10^EC503))^n3);
IR_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
-IR_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2);
+IR_2 = min2
********** MODEL REACTIONS
+v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
+v4b = k4b * BETAa
+v3a = (k3a*IR_3*adr+k3a2)*(1-phe_effect*phe)*ALPHA
+v3b = k3b * ALPHAa
+
v1a = (k1a*cAMP+k1a2*IR_1)*PKB
v1b = k1b*PKBp
v2a = k2a*PKBp*PDE3B
v2b = k2b * PDE3Ba*IR_2
-v3a = (k3a*IR_3)*adr*(1-phe)*ALPHA
-v3b = k3b * ALPHAa
-v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
-v4b = k4b * BETAa
v5a = k5a * (BETAa) * AC
-v5b = k5b * (ALPHAa) * ACa
-v6a = k6basal + k6a*ACa
-v6b = (k6b*PDE3Ba + k6b2)*cAMP
+v5b = (k5b * ALPHAa) * ACa
+v6a = k6a*ACa
+v6b = (k6b*PDE3Ba)*cAMP
v7a = k7a*cAMP*HSL
v7b = k7b * HSLp
v8a = k8a * HSLp
v8b = k8b * Gly
-v8c = (k8b+k8c*diab) * FFA
+v8c = (k8b+k8c*(1-diab)) * FFA
********** MODEL FUNCTIONS
********** MODEL EVENTS
diff --git a/Scripts and data/lipolysis_noIns3.txt b/Scripts and data/lipolysis_noIns3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95ec75b2bb06b678153366f00fcf1173f73c4cc5
--- /dev/null
+++ b/Scripts and data/lipolysis_noIns3.txt
@@ -0,0 +1,118 @@
+********** MODEL NAME
+lipolysis_noIns3
+********** MODEL NOTES
+
+********** MODEL STATES
+d/dt(BETA) = -v4a + v4b
+d/dt(BETAa) = v4a - v4b
+d/dt(ALPHA) = -v3a + v3b
+d/dt(ALPHAa) = v3a - v3b
+d/dt(AC) = -v5a + v5b
+d/dt(ACa) = v5a - v5b
+d/dt(PKB) = -v1a + v1b
+d/dt(PKBp) = v1a - v1b
+d/dt(PDE3B) = -v2a + v2b
+d/dt(PDE3Ba) = v2a - v2b
+d/dt(cAMP) = v6a - v6b
+d/dt(HSL) = -v7a + v7b
+d/dt(HSLp) = v7a - v7b
+d/dt(Gly) = v8a - v8b
+d/dt(FFA) = 3*v8a - v8c
+
+BETA(0) = 80
+BETAa(0) = 20
+ALPHA(0) = 80
+ALPHAa(0) = 20
+AC(0) = 80
+ACa(0) = 20
+PKB(0) = 80
+PKBp(0) = 20
+PDE3B(0) = 80
+PDE3Ba(0) = 20
+cAMP(0) = 0
+HSL(0) = 80
+HSLp(0) = 20
+Gly(0) = 0
+FFA(0)= 0
+********** MODEL PARAMETERS
+kdrift=0
+k4a = 62
+k4a2 = 0
+k4b = 0.12
+
+k3b = 1e-5
+k3a = 0.0004
+k3a2 = 1
+k5a = 0.0025
+k5b = 4.6
+
+k1a=1
+k1a2=1
+k1b=1
+
+k2a = 2.1
+k2b = 7.3
+k6a = 0.2
+k6b = 0.05
+k7a = 0.3
+k7b = 0.07
+k8a = 20
+k8c = 1 % reesterification parameter
+k8b = 1e-3
+
+phe_effect = 1
+
+isoscale = 10
+
+min3=0
+min1=0
+min2=0
+
+EC501=7
+EC502=1
+n1=8
+n2=1
+
+diab = 0
+
+phe = 0
+adr = 0
+iso = 0
+insulin = 37.2
+
+
+********** MODEL VARIABLES
+y_PKB=PKBp
+y_Glycerol=Gly-kdrift*time
+y_HSL=HSLp
+y_FFA=FFA-kdrift*time
+
+IR_3 = min3;
+IR_1 = 100+(min1-100)/(1+(insulin/(10^EC501))^n1);
+IR_2 = 100+(min2-100)/(1+(insulin/(10^EC502))^n2);
+
+********** MODEL REACTIONS
+v4a = (k4a * (iso*isoscale + adr) + k4a2) * BETA
+v4b = k4b * BETAa
+v3a = (k3a*IR_3*adr+k3a2)*(1-phe_effect*phe)*ALPHA
+v3b = k3b * ALPHAa
+
+v1a = (k1a*cAMP+k1a2*IR_1)*PKB
+v1b = k1b*PKBp
+v2a = k2a*PKBp*PDE3B
+v2b = k2b * PDE3Ba*IR_2
+v5a = k5a * (BETAa) * AC
+v5b = (k5b * ALPHAa) * ACa
+v6a = k6a*ACa
+v6b = (k6b*PDE3Ba)*cAMP
+v7a = k7a*cAMP*HSL
+v7b = k7b * HSLp
+v8a = k8a * HSLp
+v8b = k8b * Gly
+v8c = (k8b+k8c*(1-diab)) * FFA
+********** MODEL FUNCTIONS
+
+********** MODEL EVENTS
+
+********** MODEL MATLAB FUNCTIONS
+
diff --git a/Scripts and data/simulateDR.m b/Scripts and data/simulateDR.m
deleted file mode 100644
index d0dd3ef4ee122c3fc366252d945cb16e8a69ee31..0000000000000000000000000000000000000000
--- a/Scripts and data/simulateDR.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function [responses, responsesRaw, basal] = simulateDR(model, params, expInd, doDiabetes, diab, stimulus, plotTS)
-if nargin<7 || isempty(plotTS); plotTS=false; end
-
-params(1)=0; % The parameter for drift in in vivo data
-params(expInd)=0;% The parameter for clearance of glycerol/FFA
-
-[response, ~, sims]=simulate_dose_response(model,params ,[1],stimulus);
-responsesRaw.Normal=response;
-vars={'cAMP','Glycerol','FFA','HSL','PKB'};
-basal=response(1,vars);
-response{:,vars}=response{:,vars}./basal{1,vars}*100;
-
-responses.Normal=response;
-if plotTS
- figure(20)
- c=[jet(size(sims,2)-1); 0 0 0];
- variables=IQMvariables(model);
- observables=strrep(variables(contains(variables,'y_')),'y_',''); % Find variables containing "y_", and then remove that string from those variables.
-
- for i=1:size(sims,2)
- for j=1:length(observables)
- subplot(3,3,j)
- hold on
- plot(sims(i).time, sims(i).variablevalues(:,j),'color',c(i,:))
- title(observables{j})
- end
- end
-end
-
-if doDiabetes %Only do the diabetes simulation, if the diabets parameters is smaller than one, with 1e-1 tolerance due to precision
- [response]=simulate_dose_response(model,params, diab,stimulus);
- response{:,vars}=response{:,vars}./basal{1,vars}*100;
- responses.Diabetes=response;
-end
-
-end
-function [response, basal, sims] = simulate_dose_response(model, params, diab,stimulus)
-% Steady state
-variables=IQMvariables(model);
-observables=strrep(variables(contains(variables,'y_')),'y_',''); % Find variables containing "y_", and then remove that string from those variables.
- inputs=variables(~contains(variables,'y_'));
- response=array2table(nan(height(stimulus),length(observables)),'VariableNames',observables);
- IR=array2table(nan(height(stimulus),length(inputs)),'VariableNames',inputs);
- washInd=ismember(IQMstates(model),{'Gly','FFA'});
- % sims(height(stimulus))=struct();
- for i = fliplr(1:height(stimulus))
- sim0=model([0 10000],[],[params diab 0 0 0 0]); % simulate rest before start of experiment
- ic=sim0.statevalues(end,:);
- ic(washInd)=0; % wash away extracellular stuff
- sim0=model((0:15)',ic,[params diab 0 0 0 0]); %simulate 15 minutes without stimulus
-
-
- simIncub=model((0:30)',sim0.statevalues(end,:),[params diab 0 0 0 0]);% Others
- simIns=model((simIncub.time(end):simIncub.time(end)+15)',simIncub.statevalues(end,:),[params diab 0 0 0 stimulus.Ins(i)]);%
- simIso=model((simIns.time(end):simIns.time(end)+10)',simIns.statevalues(end,:),[params diab 0 0 stimulus.Iso(i) stimulus.Ins(i)]);
-
- response{i,:}=simIso.variablevalues(end,1:length(observables));
-
- IR{i,:}=simIso.variablevalues(end,end-length(inputs)+1:end);
- sims(i)=StructFieldCat(simIncub, [simIns simIso]);
- if i==1
- basal=simIso.statevalues(end,:);
- end
- end
- response=[stimulus(:,1:2) IR response];
- end
-
-
-% end
\ No newline at end of file
diff --git a/Scripts and data/simulateInVitro.m b/Scripts and data/simulateInVitro.m
new file mode 100644
index 0000000000000000000000000000000000000000..edc7dd94af7c37d1201c5b77b7104cdcc65b1ad2
--- /dev/null
+++ b/Scripts and data/simulateInVitro.m
@@ -0,0 +1,76 @@
+function [responses, steady, responsesRaw, basal] = simulateInVitro(model, params, expInd, diab, stimulus, plotTS)
+if nargin<6 || isempty(plotTS); plotTS=false; end
+
+params(1)=0; % The parameter for drift in in vivo data
+params(expInd)=0;% The parameter for clearance of glycerol/FFA
+
+[response, ~, steady, sims]=simulate_dose_response(model,params ,0,stimulus);
+responsesRaw.Normal=response;
+vars={'Glycerol','FFA','HSL','PKB'};
+basal=response(1,vars);
+response{:,vars}=response{:,vars}./basal{1,vars}*100;
+
+responses.Normal=response;
+if plotTS
+ figure(20)
+ c=[jet(size(sims,2)-1); 0 0 0];
+ variables=IQMvariables(model);
+ observables=strrep(variables(contains(variables,'y_')),'y_',''); % Find variables containing "y_", and then remove that string from those variables.
+
+ for i=1:size(sims,2)
+ for j=1:length(observables)
+ subplot(3,3,j)
+ hold on
+ plot(sims(i).time, sims(i).variablevalues(:,j),'color',c(i,:))
+ title(observables{j})
+ end
+ end
+end
+
+if diab~=0
+ responses.Normal.Reesterification = 100 * (3*responses.Normal.Glycerol - responses.Normal.FFA)./(3*responses.Normal.Glycerol);
+ [response]=simulate_dose_response(model,params, diab,stimulus);
+ responsesRaw.Diabetes=response;
+ response{:,vars}=response{:,vars}./basal{1,vars}*100;
+ responses.Diabetes=response;
+ responses.Diabetes.Reesterification = 100 * (3*responses.Diabetes.Glycerol - responses.Diabetes.FFA)./(3*responses.Diabetes.Glycerol);
+
+end
+
+end
+function [response, basal, steady, sims] = simulate_dose_response(model, params, diab,stimulus)
+
+variables=IQMvariables(model);
+observables=strrep(variables(contains(variables,'y_')),'y_',''); % Find variables containing "y_", and then remove that string from those variables.
+inputs=variables(~contains(variables,'y_'));
+response=array2table(nan(height(stimulus),length(observables)),'VariableNames',observables);
+IR=array2table(nan(height(stimulus),length(inputs)),'VariableNames',inputs);
+washInd=ismember(IQMstates(model),{'Gly','FFA'});
+
+sim0=model([0 30000-10 30000],[],[params diab 0 0 0 0]); % simulate rest before start of experiment
+steady = abs(sim0.statevalues(end-1,1:end-2)-sim0.statevalues(end,1:end-2)); % ignores FFA and Glycerol released since they are washed away
+steady = sum(steady(steady>1e-6))*1e9;
+ic=sim0.statevalues(end,:);
+ic(washInd)=0; % wash away extracellular stuff
+
+for i = fliplr(1:height(stimulus))
+
+ sim0=model((0:15)',ic,[params diab 0 0 0 0]); %simulate 15 minutes without stimulus
+
+ simIncub=model((0:30)',sim0.statevalues(end,:),[params diab 0 0 0 0]);% Others
+ simIns=model((simIncub.time(end):simIncub.time(end)+15)',simIncub.statevalues(end,:),[params diab 0 0 0 stimulus.Ins(i)]);%
+ simIso=model((simIns.time(end):simIns.time(end)+10)',simIns.statevalues(end,:),[params diab 0 0 stimulus.Iso(i) stimulus.Ins(i)]);
+
+ response{i,:}=simIso.variablevalues(end,1:length(observables));
+
+ IR{i,:}=simIso.variablevalues(end,end-length(inputs)+1:end);
+ sims(i)=StructFieldCat(simIncub, [simIns simIso]);
+ if i==1
+ basal=simIso.statevalues(end,:);
+ end
+end
+response=[stimulus(:,1:2) IR response];
+end
+
+
+% end
\ No newline at end of file