Team:Paris/Modeling/More f1 Algo

From 2008.igem.org

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== find_&#131;1 ==
== find_&#131;1 ==
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<html><pre class="codeinput">
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<span class="keyword">function</span> optimal_parameters = find_f1(X_data, Y_data, initial_parameters)
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<span class="comment">% gives the 'best parameters' involved in f1 by least-square optimisation
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</span>
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<span class="comment">% X_data = vector of given values of a [aTc]i (experimentally
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</span><span class="comment">% controled)
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</span><span class="comment">% Y_data = vector of experimentally measured values f1 corresponding of
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</span><span class="comment">% the X_data
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</span><span class="comment">% initial_parameters = values of the parameters proposed by the literature
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</span><span class="comment">%                      or simply guessed
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</span><span class="comment">%                    = [beta1, (K20 -> (gamma.K20)/(coefTet.f0)), n20, K19, n19]
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</span>
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<span class="comment">% Warning : in the global parameters, K20 -> K20/coefTet
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</span>
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    <span class="keyword">function</span> output = expr_pTet(parameters, X_data)
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        <span class="keyword">for</span> k = 1:length(X_data)
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                output(k) = parameters(1) * (1 - ...
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                    hill((1 - hill(X_data(k),parameters(4),parameters(5))),parameters(2),parameters(3)));
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        <span class="keyword">end</span>
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    <span class="keyword">end</span>
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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);
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<span class="comment">% options for the function lsqcurvefit
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</span>
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optimal_parameters = lsqcurvefit( @(parameters, X_data) expr_pTet(parameters, X_data), ...
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    initial_parameters, X_data, Y_data, 1/10*initial_parameters, 10*initial_parameters, options );
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<span class="comment">% search for the fittest parameters, between 1/10 and 10 times the initial
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</span><span class="comment">% parameters
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</span>
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<span class="keyword">end</span>
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</pre></html>
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== Inv_&#131;1 ==
== Inv_&#131;1 ==
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<html><pre class="codeinput">
 
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<span class="keyword">function</span> quant_aTc = Inv_f1(inducer_quantity,aTc_0)
 
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<span class="comment">% gives the quantity of [aTc]i needed to get inducer_quantity of a protein
 
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</span><span class="comment">% throught a gene behind pTet
 
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</span>
 
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<span class="keyword">global</span> gamma, f0;
 
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<span class="comment">% parameters
 
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</span>
 
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    <span class="keyword">function</span> equa = F(x)
 
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        equa = f1( (f0/gamma) , x ) - inducer_quantity;
 
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    <span class="keyword">end</span>
 
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options=optimset(<span class="string">'LevenbergMarquardt'</span>,<span class="string">'on'</span>,<span
 
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class="string">'TolX'</span>,1e-10,<span class="string">'MaxFunEvals'</span>,1e10,<span
 
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class="string">'TolFun'</span>,1e-10,<span class="string">'MaxIter'</span>,1e4);
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</div>
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quant_aTc = fsolve(F,aTc_0,options);
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<span class="keyword">end</span>
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</pre></html></div>
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Revision as of 11:03, 29 October 2008

find_ƒ1

Inv_ƒ1

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