Team:Paris/Modeling/f5

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Method & Algorithm : ƒ5


Specific Plasmid Characterisation for ƒ5

We have [FlhDC]real = {coefflhDC}expr(pTet) = {coefflhDC} ƒ1([aTc]i) and [FliA]real = {coeffliA}expr(pBad) = {coeffliA} ƒ2([arab]i)

but we use [aTc]i = Inv_ƒ1( [FlhDC] ) and [arab]i = Inv_ƒ2( [FliA] )

So, at steady-states,

F5.jpg




↓ Table ↑


param signification unit value comments
(fluorescence) value of the observed fluorescence au need for 20 mesures with well choosen values of [aTc]i
and for 20 mesures with well choosen values of [arab]i
and 5x5 measures for the relation below?
conversion conversion ratio between
fluorescence and concentration
↓ gives ↓ 		nM.au-1 (1/79.429)
[GFP] GFP concentration at steady-state nM
γGFP dilution-degradation rate
of GFP(mut3b)
↓ gives ↓ 		min-1 0.0198 Time Cell Division : 35 min.
ƒ5 activity of
pFliL with RBS E0032 		nM.min-1



param signification
corresponding parameters in the equations 		unit value comments
β24 total transcription rate of
FlhDC><pFliL with RBS E0032
β24 		nM.min-1
(K2/{coeffliA}) activation constant of FlhDC><pFliL
K2 		nM
n2 complexation order of FlhDC><pFliL
n2 		no dimension
β25 total transcription rate of
FliA><pFliL with RBS E0032
β25 		nM.min-1
(K8/{coefflhDC}) activation constant of FliA><pFliL
K8 		nM
n8 complexation order of FliA><pFliL
n8 		no dimension


↓ Algorithm ↑


find_ƒP 

function optimal_parameters = find_FP(X_data, Y_data, initial_parameters)
% gives the 'best parameters' involved in f4, f5, f6, f7 or f8  
% with FlhDC = 0 or FliA = 0 by least-square optimisation
 
% X_data = vector of given values of [FliA]i or [FlhDC]i (experimentally
% controled)
% Y_data = vector of experimentally measured values f4, f5, f6, f7 or f8
% corresponding of the X_data
% initial_parameters = values of the parameters proposed by the literature
%                       or simply guessed
%                    = [beta, K -> (K)/(coef), n]
 
     function output = act_pProm(parameters, X_data)
         for k = 1:length(X_data)
                 output(k) = parameters(1)*hill(X_data(k), 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) act_pProm(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

Then, if we have time, we want to verify the expected relation

SumpFliL.jpg


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