Support:Documents:Examples:Estimate physiological parameters using a physiologically based model

Estimation of Physiological Parameters Using a Physiologically Based Model

Overview

To evalute glucose trasnport and phosphorylation in skeletal muscle,a physiologically based model has been proposed by our group. The below figure shows the kinetics of a phosphorylatable glucsoe analog (e.g. ¹⁸F-labeled 2-fluoro-2-deoxy-D-glucose) in skeletal muscle. The model has three tissue compartments with five rate constants.

k_{1 and k2 describe the passive exhange of the glucose analog between plasma and interstitial space.}

Example of Implementation of the Physiologically Based Model

Here, we show an example of implementation of the proposed model using COMKAT.

cm=compartmentModel;

% Define parameters
cm=addParameter(cm,'k1',0.1);                                             % Diffusion of glucose analog from plasma to interstitial space
cm=addParameter(cm,'k2','k1/Fis');                                        % Diffusion of glucose analog from interstitial space to plasma
cm=addParameter(cm,'k3','VG/(Fis*KGa+Fis*ISg*KGa/KGg)'); % Inward transport of glucose analog
cm=addParameter(cm,'k4','VG/(Fic*KGa+Fic*ICg*KGa/KGg)'); % Backward transport of glucose analog
cm=addParameter(cm,'k5','VH/(Fic*KHa+Fic*ICg*KHa/KHg)');  % Phosphorylation of glucose analog

cm=addParameter(cm,'Fb',0.02);         % fraction of total space occupied by blood
cm=addParameter(cm,'Fis',0.3);          % fraction of total space occupied by interstitial space
cm=addParameter(cm,'Fic','1-Fis-Fb');  % fraction of total space occupied by intracellular space
cm=addParameter(cm,'F',1);

cm=addParameter(cm,'Pg',6);     % Plasma glucose concentration (mM)
cm=addParameter(cm,'ISg',5.4); % Interstitial glucose concentration (mM)
cm=addParameter(cm,'ICg',0.2); % Intracellular glucose concentration (mM)

cm=addParameter(cm,'KGg',3.5); % Michaelis constant (mM) of glucose transporter (GLUT) for glucose 
cm=addParameter(cm,'KHg',0.13); % Michaelis constant (mM) of hexokinase (HK) for glucose 

cm=addParameter(cm,'KGa',14); % Michaelis constant (mM) of GLUT for glucose analog 
cm=addParameter(cm,'KHa',0.17); % Michaelis constant (mM) of HK for glucose analog 

cm=addParameter(cm,'VG','(k1*Pg-k1*ISg)/(ISg/(KGg+ISg)-ICg/(KGg+ICg))'); % Maximal velocity of glucose transport for glucose and its analogs
cm=addParameter(cm,'VH','(k1*Pg-k1*ISg)/(ICg/(KHg+ICg))'); % Maximal velocity of glucose phosphorylation for glucose and its analogs

% Specific activity (sa): if the unit of image data is the same with that of input function, the specific activity is 1.
cm=addParameter(cm,'sa',1); 
% Usually, the decay time correction of image data is performed. So, dk is zero. 
cm=addParameter(cm,'dk',0);

% Define scan time (minutes)
delay = 0.0;
scanduration = 120; 

t=[ones(5,1)/30;ones(10,1)/12;ones(12,1)*0.5;ones(8,1);ones(21,1)*5];
scant = [[0;cumsum(t(1:(length(t)-1)))] cumsum(t)];
scanTime  = [scant(:,1),scant(:,2)];

cm = set(cm, 'ScanTime', scanTime);

lambda = [-12.02 -2.57 -0.02];

a = [1771.5 94.55 14.27];

cm = addParameter(cm, 'pfeng', [delay a lambda]');

% Define input function
cm = addInput(cm, 'Cp', 'sa', 'dk', 'fengInputByPar','pfeng'); % Cp is the plasma input function
cm = addInput(cm, 'Ca',1,0, 'fengInputByPar', 'pfeng'); % Ca is the decay-corrected whole-blood input function. Herein, we assume that Cp=Ca.

% Define compartment
cm=addCompartment(cm,'IS'); % interstitial 
cm=addCompartment(cm,'IC'); % intracellular
cm=addCompartment(cm,'IP'); % intracellular phosphorylated 
cm=addCompartment(cm,'Junk');

% Define link
cm=addLink(cm,'L','Cp','IS','k1');
cm=addLink(cm,'K','IS','Junk','k2');
cm=addLink(cm,'K','IS','IC','k3');
cm=addLink(cm,'K','IC','IS','k4');
cm=addLink(cm,'K','IC','IP','k5');

% Define output obtained from each normalized compartment
wlistTotal={'IS','F';'IC','F';'IP','F'};
xlistTotal={'Ca','Fb'};
cm=addOutput(cm,'TissueTotal',wlistTotal,xlistTotal);

% Define model parameters to be esimated
cm=addSensitivity(cm,'k1','ISg','ICg','Fis','Fb'); 

[PET,PETIndex,Output,OutputIndex]=solve(cm);

noise_level=0.05;

sd=noise_level*sqrt(PET(:,3)./(PET(:,2)-PET(:,1)));

data=sd.*randn(size(PET(:,3)))+PET(:,3);

cm=set(cm,'ExperimentalData',data);

cm = set(cm,'ExperimentalDataSD',sd);

optsIRLS = setopt('SDModelFunction', @IRLSnoiseModel);
cm = set(cm, 'IRLSOptions', optsIRLS);

oo = optimset('TolFun', 1e-8, 'TolX', 1e-4,'Algorithm','interior-point');
cm = set(cm, 'OptimizerOptions', oo);

% Set initial conditions and bounds
%        k1,    ISg,   ICg,     Fs,     Fb
pinit = [0.01  ; 4.4 ; 0.1    ;0.15 ; 0.01];
plb   = [0.001; 1    ; 0.001;0.10 ; 0     ];
pub  = [0.5   ; 6    ; 1       ;0.60 ; 0.04];

[pfit, qfit, modelfit, exitflag, output, lambda, grad, hessian, objfunval] = fitGen(cm, pinit, plb, pub, 'IRLS'); 

figure;
t = 0.5*(PET(:,1)+PET(:,2));
plot(t,PET(:,3),'g-',t,data,'ro','LineWidth',2);
xlabel('Time (minutes)');
ylabel('Concentrations (uCi/mL)');
legend('Noisy data','Noise-free data');

figure;
t = 0.5*(PET(:,1)+PET(:,2));
plot(t,data,'ro',t,modelfit,'b-','LineWidth',2);
xlabel('Time (minutes)');
ylabel('Concentrations (uCi/mL)');
legend('Noisy data', 'Model fit');

Pg=6;KGa=14;KHa=0.17;KGg=3.5;KHg=0.13; % Units are mM

k1=pfit(1);k2=pfit(1)/pfit(4);ISg=pfit(2);ICg=pfit(3);Fis=pfit(4);Fb=pfit(5);

% Physiological parameters 

VG=(k1*ISg-k1*Pg)/(ICg/(KGg+ICg)-ISg/(KGg+ISg))      
 
VH=(k1*Pg-k1*ISg)/(ICg/(KHg+ICg))            
           
CI=VG*ISg/(KGg+ISg)           
                           
CE=VG*ICg/(KGg+ICg)    
                               
PR=VH*ICg/(KHg+ICg) 
                            

Results