 %  DOQCS : http://doqcs.ncbs.res.in/ 
 %  Accession Name = L-Arginine 
 %  Accession Number = 72 
 %  Transcriber = Sharat J. Vayttaden and Prasoon Agarwal, NCBS 
 %  Developer = Abu-Soud HM, Wang J, Rousseau DL, and Stuehr DJ 
 %  Species = Human 
 %  Tissue = Kidney cell expressing the rat brain nNOS 
 %  Cell Compartment = Cytosol 
 %  Notes = This model was taken from the <a href=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=10493814&query_hl=10&itool=pubmed_docsum>Abu-Soud HM et al. Biochemistry. 1999 Sep 21;38(38):12446-51</a>. This model shows kinetic binding of L-Arginine to neuronal nitric oxide synthase.<br> Model shows the substrate (L-Arginine) binding to nNOS in a two-step reversible fashion. First there is rapid binding equilibrium between Im-nNOS and L-Arginine to form an intermediate that contains bound imidazole and L-Arginine. This is followed by a slower conformational change in the Im-enzyme-substrate complex that is associated with release of bound imidazole and generation of a modified enzyme-substrate complex which is detected due to spectral change. Replicates Figure 4 (right panel).
 
 
   % Note: All the concentrations are in micro Mole (uM)

function moose_convert
%Matlab simulation for chemical kinetics

% Saved on Thu Aug  3 14:51:29 2006
% Automatically generated from kkit model in MOOSE by conv_matlab

global buff;
global t;
global y;

% Define Tables here
tspan=[0, 0.09];
% Molecule names
molnames = {
'1 /kinetics/Im-nNOS'
'2 /kinetics/L-Arginine'
'3 /kinetics/Im-nNOS-L-Arginine'
'4 /kinetics/nNOS-L-Arginine'
'5 /kinetics/Imidazole'
};

bufnames = {
};

sumtotnames = {
};

% Initial concs of 5 molecules
y0(1) = 1;
y0(2) = 6000;
y0(3) = 0;
y0(4) = 0;
y0(5) = 0;

% 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(:,1));
%plot in matlab
temp = [t, y(:, 4)];
save('/home/prasoon/models/nNOS/Files_to_upload/Arg_new/plot1/nNOS_L-Arg_m.dat','temp','-ASCII');
%plot(t,y(:,3));
%plot(t,y(:,5));
%plot(t,y(:,2));
plot(t,y(:,4));

% Evaluation function
function dydt = f(t, y)
global buff;
dydt = [
 - 250 * y(2) * y(1) + 125000 * y(3)
 - 250 * y(2) * y(1) + 125000 * y(3)
 + 250 * y(2) * y(1) - 125000 * y(3) - 81 * y(3) + 0.45 * y(4) * y(5)
 + 81 * y(3) - 0.45 * y(4) * y(5)
 + 81 * y(3) - 0.45 * y(4) * y(5)
];

% end of model
