 %  DOQCS : http://doqcs.ncbs.res.in/ 
 %  Accession Name = PLCbeta1 
 %  Accession Number = 13 
 %  Transcriber = Sridhar Hariharaputran, NCBS 
 %  Developer = Sridhar Hariharputran, NCBS 
 %  Species = Bovine; Mammalian 
 %  Tissue = Brain - Neuronal; expressed in E.coli 
 %  Cell Compartment = Cell Membrane 
 %  Notes =  This is a model of PLCbeta1 in the bovine brain membranes as stimulated by Ca, Gq and PA. <br>The primary data sources are <a href= http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=10869178 >Irene Litosch Biochemistry (2000) 39(26):7736-43</a>, <br><a href= http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=1351294 >Sternweis PC et al. Philos Trans R Soc Lond B Biol Sci. (1992) 336(1276):35-41</a> and <br><a href= http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=1846707 >Smrcka AV et al. Science (1991) 251(4995):804-7</a>. 
 
   % Note: All the concentrations are in micro Mole (uM)

function moose_convert
%Matlab simulation for chemical kinetics

% Saved on Tue Feb  1 11:54:35 2005

% Automatically generated from kkit model in MOOSE by conv_matlab

global buff;
tspan=[0, 50];
% Molecule names
molnames = {
'1 /kinetics/PLCbeta/PLCbeta'
'2 /kinetics/PLCbeta/Ca.PLCbeta'
'3 /kinetics/PLCbeta/PA.Ca.PLCbeta'
'4 /kinetics/PLCbeta/Gq'
'5 /kinetics/PLCbeta/PA'
'6 /kinetics/PLCbeta/DAG'
'7 /kinetics/PLCbeta/IP3'
'8 /kinetics/PLCbeta/Gq.Ca.PLCbeta'
'9 /kinetics/PLCbeta/Gq.Ca.PLCbeta/Gq.Ca.PLCbeta/Gq.Ca.PLCbeta_cplx'
};

bufnames = {
'1 /kinetics/PLCbeta/PIP2'
'2 /kinetics/PLCbeta/Ca'
};

sumtotnames = {
};

% Initial concs of 9 molecules
y0(1) = 0.12;
y0(2) = 0;
y0(3) = 0;
y0(4) = 0;
y0(5) = 1;
y0(6) = 0;
y0(7) = 0;
y0(8) = 0;
y0(9) = 0;
buff(1) = 25;
buff(2) = 0.09;
% Call the ODE solver and display results
[t, y] = ode23s(@f, tspan, y0);

hold on;
plot(t,buff(:,2)*ones(size(t)));
plot(t,y(:,1));
plot(t,y(:,7));
plot(t,y(:,5));
plot(t,y(:,6));
% Evaluation function
function dydt = f(t, y)
global buff;
dydt = [
 - 1 * buff(2) * y(1) + 1 * y(2)
 + 1 * buff(2) * y(1) - 1 * y(2) - 50 * y(4) * y(2) + 1 * y(8) - 1 * y(5) * y(2) + 8 * y(3)
 + 1 * y(5) * y(2) - 8 * y(3)
 - 50 * y(4) * y(2) + 1 * y(8)
 - 1 * y(5) * y(2) + 8 * y(3)
 + 7 * y(2) * buff(1) / ( 10 + buff(1) ) + 23 * y(9) + 38 * y(3) * buff(1) / ( 10 + buff(1) ) + 0.009 * buff(1)
 + 7 * y(2) * buff(1) / ( 10 + buff(1) ) + 23 * y(9) + 38 * y(3) * buff(1) / ( 10 + buff(1) ) + 0.009 * buff(1)
 + 50 * y(4) * y(2) - 1 * y(8) - 11.5 * buff(1) * y(8) + 92 * y(9) + 23 * y(9)
 + 11.5 * buff(1) * y(8) - 92 * y(9) - 23 * y(9)
];

% end of model
