Team:Paris/Modeling/More f1 Algo
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<html><pre class="codeinput"> | <html><pre class="codeinput"> | ||
- | <span class="keyword">function</span> | + | <span class="keyword">function</span> optimal_parameters = find_f1(X_data, Y_data, initial_parameters) |
- | <span class="comment">% | + | <span class="comment">% gives the 'best parameters' involved in f1 by least-square optimisation |
</span> | </span> | ||
- | <span class=" | + | <span class="comment">% X_data = vector of given values of a [aTc]i (experimentally |
- | <span class="comment">% parameters | + | </span><span class="comment">% controled) |
+ | </span><span class="comment">% Y_data = vector of experimentally measured values f1 corresponding of | ||
+ | </span><span class="comment">% the X_data | ||
+ | </span><span class="comment">% initial_parameters = values of the parameters proposed by the literature | ||
+ | </span><span class="comment">% or simply guessed | ||
+ | </span><span class="comment">% = [beta16, (K13 -> (gamma.K13)/(coefTet.f0)), n13, K12, n12] | ||
</span> | </span> | ||
- | + | <span class="comment">% Warning : in the global parameters, K20 -> K20/coefTet | |
+ | </span> | ||
+ | <span class="keyword">function</span> output = expr_pTet(parameters, X_data) | ||
+ | <span class="keyword">for</span> k = 1:length(X_data) | ||
+ | output(k) = parameters(1) * (1 - ... | ||
+ | hill((1 - hill(X_data(k),parameters(4),parameters(5))),parameters(2),parameters(3))); | ||
+ | <span class="keyword">end</span> | ||
+ | <span class="keyword">end</span> | ||
+ | options=optimset(<span class="string">'LevenbergMarquardt'</span>,<span class="string">'on'</span>,<span class="string">'TolX'</span>,1e-10,<span class="string">'MaxFunEvals'</span>,1e10,<span class="string">'TolFun'</span>,1e-10,<span class="string">'MaxIter'</span>,1e4); | ||
+ | <span class="comment">% options for the function lsqcurvefit | ||
+ | </span> | ||
+ | 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 ); | ||
+ | <span class="comment">% search for the fittest parameters, between 1/10 and 10 times the initial | ||
+ | </span><span class="comment">% parameters | ||
+ | </span> | ||
<span class="keyword">end</span> | <span class="keyword">end</span> | ||
</pre></html> | </pre></html> | ||
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== Inv_ƒ1 == | == Inv_ƒ1 == | ||
- | + | <html><pre class="codeinput"> | |
+ | <span class="keyword">function</span> quant_aTc = Inv_f1(inducer_quantity) | ||
+ | <span class="comment">% gives the quantity of [aTc]i needed to get inducer_quantity of a protein | ||
+ | </span><span class="comment">% throught a gene behind pTet | ||
+ | </span> | ||
+ | <span class="keyword">global</span> gamma, f0; | ||
+ | <span class="comment">% parameters | ||
+ | </span> | ||
+ | <span class="keyword">function</span> equa = F(x) | ||
+ | equa = f1( (f0/gamma) , x ) - inducer_quantity; | ||
+ | <span class="keyword">end</span> | ||
+ | |||
+ | options=optimset(<span class="string">'LevenbergMarquardt'</span>,<span class="string">'on'</span>,<span class="string">'TolX'</span>,1e-10,<span class="string">'MaxFunEvals'</span>,1e10,<span class="string">'TolFun'</span>,1e-10,<span class="string">'MaxIter'</span>,1e4); | ||
+ | |||
+ | quant_aTc = fsolve(F,1,options); | ||
+ | |||
+ | <span class="keyword">end</span> | ||
+ | </pre></html> | ||
</div> | </div> |
Latest revision as of 03:47, 30 October 2008
find_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_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