Team:Paris/Modeling/f3bis

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{{Paris/Header|Method & Algorithm : ƒ3bis}}
{{Paris/Header|Method & Algorithm : ƒ3bis}}
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<center> = act_''pFlhDC'' </center>
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<br>
[[Image:f6DCA.png|thumb|Specific Plasmid Characterisation for &#131;3bis]]
[[Image:f6DCA.png|thumb|Specific Plasmid Characterisation for &#131;3bis]]
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In this experiment, we have ''' [''EnvZ'']<sub>''real''</sub> = {coef<sub>''envZ''</sub>} &#131;1([aTc]<sub>i</sub>) '''
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In this experiment, we have  
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''' [''EnvZ'']<sub>''real''</sub> = {coef<sub>''envZ''</sub>} &#131;1([aTc]<sub>i</sub>) '''
but we use ''' [aTc]<sub>i</sub> = Inv_&#131;1( [''EnvZ''] ) '''
but we use ''' [aTc]<sub>i</sub> = Inv_&#131;1( [''EnvZ''] ) '''

Revision as of 02:06, 30 October 2008

Method & Algorithm : ƒ3bis


= act_pFlhDC


Specific Plasmid Characterisation for ƒ3bis

In this experiment, we have

[EnvZ]real = {coefenvZ} ƒ1([aTc]i)

but we use [aTc]i = Inv_ƒ1( [EnvZ] )

so, at steady-states, phosphorylated OmpR verify :

F3b.jpg

We can then solve it, and reintriduce the result in the previously characterized ƒ3( 0, [OmpR*] ) , to determine the parameters :

↓ Table of Values ↑


Team:Paris/Modeling/More f3bis Table

↓ Algorithm ↑


function optimal_parameters = find_f3_EnvZ(X_data, Y_data, initial_parameters)
% gives the 'best parameters' involved in f3 with OmpR = 0 by least-square optimisation
% -> USE IT AFTER find_f3_OmpR
 
% X_data = vector of given values of ( [EnvZ]i ) (experimentally
% controled)
% Y_data = vector of experimentally measured values f3 corresponding of
% the X_data
% initial_parameters = values of the parameters proposed by the literature
%                       or simply guessed
%                    = [EnvZ_b, OmpR_b, K14, n14]
 
global beta17 K15 n15; % parameters GIVEN BY find_f3_OmpR
 
     function output = act_pFlhDC(parameters, X_data)
         for k = 1:length(X_data)
             OmpR_P = complexes((parameters(1) + X_data(k)),parameters(2),parameters(3),parameters(4));
                 % complexes is a function that solve the "basical
                 % complexation equation"
             output(k) = beta17*(1 - hill( OmpR_P, K15, n15 ));
         end
     end
 
options=optimset('LevenbergMarquardt','on','TolX',1e-10,'MaxFunEvals',1e10,'TolFun',1e-10,'MaxIter',1e4);
% options for the function lsqcurvefit
 
optimal_parameters = lsqcurvefit( @(parameters, X_data) act_pFlhDC(parameters, X_data), ...
     initial_parameters, X_data, Y_data, options );
% search for the fittest parameters, between 1/10 and 10 times the initial
% parameters
 
end


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