# Team:Paris/Modeling/f3bis

Method & Algorithm : ƒ3bis

= act_pFlhDC

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 :

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

↓ Table of Values ↑
↓ 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 ```