Team:Paris/Modeling/More f1 Algo



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find_&#131;1
function optimal_parameters = find_f1(X_data, Y_data, initial_parameters) % gives the 'best parameters' involved in f1 by least-square optimisation % X_data = vector of given values of a [aTc]i (experimentally % controled) % Y_data = vector of experimentally measured values f1 corresponding of % the X_data % initial_parameters = values of the parameters proposed by the literature %                      or simply guessed %                   = [beta16, (K13 -> (gamma.K13)/(coefTet.f0)), n13, K12, n12] % Warning : in the global parameters, K20 -> K20/coefTet function output = expr_pTet(parameters, X_data) for k = 1:length(X_data) output(k) = parameters(1) * (1 - ...                    hill((1 - hill(X_data(k),parameters(4),parameters(5))),parameters(2),parameters(3))); 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) expr_pTet(parameters, X_data), ...    initial_parameters, X_data, Y_data, 1/10*initial_parameters, 10*initial_parameters, options ); % search for the fittest parameters, between 1/10 and 10 times the initial % parameters end

Inv_&#131;1
function quant_aTc = Inv_f1(inducer_quantity) % gives the quantity of [aTc]i needed to get inducer_quantity of a protein % throught a gene behind pTet global gamma, f0; % parameters function equa = F(x) equa = f1( (f0/gamma), x ) - inducer_quantity; end options=optimset( 'LevenbergMarquardt', 'on' , 'TolX' ,1e-10, 'MaxFunEvals' ,1e10, 'TolFun' ,1e-10, 'MaxIter' ,1e4); quant_aTc = fsolve(F,1,options); end