 %  DOQCS : http://doqcs.ncbs.res.in/ 
 %  Accession Name = RanGTPase 
 %  Accession Number = 70 
 %  Transcriber = Sharat J. Vayttaden and Pragati Jain, NCBS 
 %  Developer = D.Gorlich, M.J.Seewald and K.Ribbeck 
 %  Species = Eukaryotic system 
 %  Tissue = HeLa Cells 
 %  Cell Compartment = Cytosol, Nucleus 
 %  Notes =  This model represents a concentration gradient of RanGTP across the nuclear envelope. This gradient is generated by distribution of regulators of RanGTPase. We have taken a log linear plot of graphs generated by GENESIS and compared with the experimental graphs.  
 
   % Note: All the concentrations are in micro Mole (uM)

function moose_convert
%Matlab simulation for chemical kinetics

% Saved on Fri May 12 21:39:07 2006
% Automatically generated from kkit model in MOOSE by conv_matlab

global buff;
global t;
global y;

% Define Tables here
tspan=[0, 25];
% Molecule names
molnames = {
'1 /kinetics/Nucleus/GDP.Ran-nuclear'
'2 /kinetics/Nucleus/RCC1-GDP.Ran'
'3 /kinetics/Nucleus/GDP'
'4 /kinetics/Nucleus/RCC1'
'5 /kinetics/Nucleus/RCC1-RanGTP'
'6 /kinetics/Nucleus/RCC1-Ran'
'7 /kinetics/Nucleus/GTP'
'8 /kinetics/Nucleus/GTP.Ran-nuclear'
'9 /kinetics/Nucleus/nuclear-NTF2-GDP.Ran'
'10 /kinetics/Cytoplasm/RanGAP'
'11 /kinetics/Cytoplasm/RanGAP/RanGAP/RanGAP_cplx'
'12 /kinetics/Cytoplasm/RanGAP/RanGAP[1]/RanGAP[1]_cplx'
'13 /kinetics/Cytoplasm/RanBP1'
'14 /kinetics/Cytoplasm/GTP.Ran-cytoplasmic'
'15 /kinetics/Cytoplasm/RanBP1.RanGTP'
'16 /kinetics/Cytoplasm/NTF2-GDP.Ran'
'17 /kinetics/Cytoplasm/GDP.Ran-cytoplasmic'
};

bufnames = {
'1 /kinetics/Cytoplasm/NTF2'
};

sumtotnames = {
};

% Initial concs of 17 molecules
y0(1) = 0;
y0(2) = 0;
y0(3) = 0;
y0(4) = 0.699996;
y0(5) = 0;
y0(6) = 0;
y0(7) = 500.007;
y0(8) = 0;
y0(9) = 0;
y0(10) = 0.7;
y0(11) = 0;
y0(12) = 0;
y0(13) = 2;
y0(14) = 0;
y0(15) = 0;
y0(16) = 0;
y0(17) = 5;
buff(1) = 0.3;

% Call the ODE solver and display results
[t, y] = ode23s(@f, tspan, y0);

hold on;
do_plots;

function do_plots()
global t;
global y;
plot(t,y(:,14));
plot(t,y(:,8));

% Evaluation function
function dydt = f(t, y)
global buff;
dydt = [
 - 74.0006 * y(1) * y(4) + 55 * y(2) + 0.48 * y(9) - 0.0450004 * y(1) * buff(1) + 0.72001 * y(16) - 0.0300002 * buff(1) * y(1)
 + 74.0006 * y(1) * y(4) - 55 * y(2) - 21 * y(2) + 11 * y(6) * y(3)
 + 21 * y(2) - 11 * y(6) * y(3)
 - 74.0006 * y(1) * y(4) + 55 * y(2) + 55 * y(5) - 99.9994 * y(8) * y(4)
 + 0.599996 * y(6) * y(7) - 19 * y(5) - 55 * y(5) + 99.9994 * y(8) * y(4)
 + 21 * y(2) - 11 * y(6) * y(3) - 0.599996 * y(6) * y(7) + 19 * y(5)
 - 0.599996 * y(6) * y(7) + 19 * y(5)
 + 55 * y(5) - 99.9994 * y(8) * y(4) - 0.08 * y(8) + 1.95003 * y(14)
 + 15000.2 * y(16) - 0.1 * y(9) - 0.48 * y(9) + 0.0450004 * y(1) * buff(1)
 - 540 * y(15) * y(10) + 43.2 * y(11) + 10.8 * y(11) - 75.7145 * y(14) * y(10) + 42.4 * y(12) + 10.6 * y(12)
 + 540 * y(15) * y(10) - 43.2 * y(11) - 10.8 * y(11)
 + 75.7145 * y(14) * y(10) - 42.4 * y(12) - 10.6 * y(12)
 - 18.0004 * y(14) * y(13) + 0.0004 * y(15)
 + 0.0533326 * y(8) - 1.3 * y(14) - 18.0004 * y(14) * y(13) + 0.0004 * y(15) - 75.7145 * y(14) * y(10) + 42.4 * y(12)
 - 540 * y(15) * y(10) + 43.2 * y(11) + 18.0004 * y(14) * y(13) - 0.0004 * y(15)
 - 0.48 * y(16) + 0.0199999 * buff(1) * y(1) + 1 * buff(1) * y(17) - 0.1 * y(16) - 10000 * y(16) + 0.0666657 * y(9)
 + 10.8 * y(11) + 10.6 * y(12) - 1 * buff(1) * y(17) + 0.1 * y(16)
];

% end of model
