Team:Groningen/Modelling/Characterization

From 2009.igem.org

(Difference between revisions)
(Ignoring most experiments and focussing on just a few variables.)
(Enabled time<Infinity measurements and adjusted the starting values a bit.)
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|id="K7sol"|
|id="K7sol"|
|-
|-
-
|tauBbeta4
+
|tauBbetaB
|id="tauBbeta4"|
|id="tauBbeta4"|
|id="tauBbeta4cur"|
|id="tauBbeta4cur"|
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|id="tauBsol"|
|id="tauBsol"|
|-
|-
-
|beta4
+
|betaB
|id="beta4"|
|id="beta4"|
|id="beta4cur"|
|id="beta4cur"|
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|id="tauRsol"|
|id="tauRsol"|
|-
|-
-
|beta1
+
|betaRN
|id="beta1"|
|id="beta1"|
|id="beta1cur"|
|id="beta1cur"|
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                   {constants:{Vc:0.006666667,Vs:(1-0.006666667),pro:0,ars2T:0,proK:1.6605e-9},AsT:20e-6,
                   {constants:{Vc:0.006666667,Vs:(1-0.006666667),pro:0,ars2T:0,proK:1.6605e-9},AsT:20e-6,
                     data:{AsinT:[2.25e-4,3.47e-4,4.19e-4,3.93e-4,4.19e-4,4.82e-4,4.82e-4,4.95e-4],
                     data:{AsinT:[2.25e-4,3.47e-4,4.19e-4,3.93e-4,4.19e-4,4.82e-4,4.82e-4,4.95e-4],
-
                           time:[582,1212,1890,2514,3144,3828,4260,6036]}},
+
                           time:[582,1212,1890,2514,3144,3828,4260,6036]}},*/
                   pSB1A2con:    //  concentration mode this is our first icps measerment wild type
                   pSB1A2con:    //  concentration mode this is our first icps measerment wild type
-
                   {constants:{Vc:0.000808081,Vs:(1-0.000808081),pro:0,ars2T:0},time:Infinity,
+
                   {constants:{Vc:0.000808081,Vs:(1-0.000808081),pro:0,ars2T:0},time:3600,
                     data:{AsinT:[207.0208222e-6,229.0443139e-6,493.3262146e-6,585.8248799e-6],
                     data:{AsinT:[207.0208222e-6,229.0443139e-6,493.3262146e-6,585.8248799e-6],
-
                           AsT:[10e-6,20e-6,50e-6,100e-6]}},*/
+
                           AsT:[10e-6,20e-6,50e-6,100e-6]}},
                   pSB1A2time:  //  concentration mode this is our first icps measerment wild type
                   pSB1A2time:  //  concentration mode this is our first icps measerment wild type
                   {constants:{Vc:0.002320346,Vs:(1-0.002320346),pro:0,ars2T:0},AsT:10e-6,
                   {constants:{Vc:0.002320346,Vs:(1-0.002320346),pro:0,ars2T:0},AsT:10e-6,
Line 255: Line 255:
                   beta5: function(v){return v.beta5;}};*/
                   beta5: function(v){return v.beta5;}};*/
-
var varsToMutate = [/*'v5_K5','v5',*/'k8_K7','k8','tauBbeta4','beta4','tauR_tauB','beta1_beta4'/*,'tauFbetaF','betaF',
+
var varsToMutate = [/*'v5_K5','v5',*/'k8_K7','k8','tauBbeta4','beta4',
 +
                    'tauRbeta1_tauBbeta4','beta1_beta4'/*,'tauFbetaF','betaF',
                     'tauKbetaK','betaK','tauGbeta5','beta5','tauF','betaF','tauK','betaK','tauG','beta5','ars2T'*/];
                     'tauKbetaK','betaK','tauGbeta5','beta5','tauF','betaF','tauK','betaK','tauG','beta5','ars2T'*/];
var mutateFuncs = {//v5: function(v){return v.v5;},
var mutateFuncs = {//v5: function(v){return v.v5;},
Line 262: Line 263:
                   K7: function(v){return v.k8/v.k8_K7;},
                   K7: function(v){return v.k8/v.k8_K7;},
                   tauB: function(v){return v.tauBbeta4/v.beta4;},
                   tauB: function(v){return v.tauBbeta4/v.beta4;},
-
                  tauR: function(v){return v.tauR_tauB*v.tauBbeta4/v.beta4;},
 
                   beta4: function(v){return v.beta4;},
                   beta4: function(v){return v.beta4;},
 +
                  tauR: function(v){return v.tauRbeta1_tauBbeta4*v.tauBbeta4/(v.beta4*v.beta1_beta4);},
 +
                  beta1: function(v){return v.beta4*v.beta1_beta4;}/*,
                   //tauF: function(v){return v.tauF;},
                   //tauF: function(v){return v.tauF;},
                   //betaF: function(v){return v.betaF;},
                   //betaF: function(v){return v.betaF;},
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                   //ars2T: function(v){return v.ars2T;},
                   //ars2T: function(v){return v.ars2T;},
                   //beta5: function(v){return v.beta5;},
                   //beta5: function(v){return v.beta5;},
-
                  beta1: function(v){return v.beta4*v.beta1_beta4;}/*,
 
                   tauF: function(v){return v.tauFbetaF/v.betaF;},
                   tauF: function(v){return v.tauFbetaF/v.betaF;},
                   betaF: function(v){return v.betaF;},
                   betaF: function(v){return v.betaF;},
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       xt = simulate(x0,e[i].data.time,function(t,d){return arsenicModelGradient(nc,d);});
       xt = simulate(x0,e[i].data.time,function(t,d){return arsenicModelGradient(nc,d);});
-
       // Sum (squares of) errors, divided by the average value
+
       // Sum of (squares of) errors, divided by the average value
       var curcost = 0, n = 0;
       var curcost = 0, n = 0;
       for(var xn in e[i].data) {
       for(var xn in e[i].data) {
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         }
         }
-
         // Sum (squares of) errors, divided by the average value
+
         // Sum of (squares of) errors, divided by the average value
         for(var xn in e[i].data) {
         for(var xn in e[i].data) {
           if (xn=='AsT') continue;
           if (xn=='AsT') continue;
           curcost += Math.pow((e[i].data[xn][j]-xt[xn])/avgv[xn],2);
           curcost += Math.pow((e[i].data[xn][j]-xt[xn])/avgv[xn],2);
 +
          n++;
 +
        }
 +
      }
 +
      cost += Math.sqrt(curcost/n); // Compute the square root of the average of the squares (RMS)
 +
      weight++;
 +
      e[i].solution.cost = Math.sqrt(curcost/n);
 +
    } else if (!isNaN(e[i].time)) { // Vary AsT, with t = e[i].time
 +
      var avgv = {};
 +
      for(var xn in e[i].data) {
 +
        avgv[xn] = 0;
 +
        for(var j in e[i].data[xn]) avgv[xn] += e[i].data[xn][j];
 +
        avgv[xn] /= e[i].data[xn].length;
 +
      }
 +
      e[i].solution = {'xt':{'AsT':[]}};
 +
      var curcost = 0, n = 0;
 +
      for(var j in e[i].data.AsT) {
 +
        // Simulate
 +
        x0 = arsenicModelInitialization(nc,e[i].data.AsT[j]);
 +
        xt = simulate(x0,e[i].time,function(t,d){return arsenicModelGradient(nc,d);});
 +
        e[i].solution.xt.AsT[j] = e[i].data.AsT[j];
 +
 +
        // Fill solution
 +
        for(var xn in xt) {
 +
          if (e[i].solution.xt[xn]==undefined) e[i].solution.xt[xn] = [];
 +
          e[i].solution.xt[xn][j] = xt[xn][xt[xn].length-1];
 +
        }
 +
 +
        // Sum of (squares of) errors, divided by the average value
 +
        for(var xn in e[i].data) {
 +
          if (xn=='AsT') continue;
 +
          curcost += Math.pow((e[i].data[xn][j]-xt[xn][xt[xn].length-1])/avgv[xn],2);
           n++;
           n++;
         }
         }
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   //cSol.v5_K5 = orgC.v5/orgC.K5;
   //cSol.v5_K5 = orgC.v5/orgC.K5;
   //cSol.v5 = orgC.v5;
   //cSol.v5 = orgC.v5;
-
   cSol.k8 = 1000;
+
   cSol.k8 = 10;
-
   cSol.k8_K7 = 1e6;
+
   cSol.k8_K7 = 2e5;
 +
  cSol.tauBbeta4 = 55;
 +
  cSol.beta4 = 18;
 +
  cSol.tauRbeta1_tauBbeta4 = 400;
 +
  cSol.beta1_beta4 = 2;
//  cSol.tauBbeta4 = 180000;
//  cSol.tauBbeta4 = 180000;
//  cSol.tauB = 180;
//  cSol.tauB = 180;
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   //c.v5_K5 = orgC.v5/orgC.K5;
   //c.v5_K5 = orgC.v5/orgC.K5;
   //c.v5 = orgC.v5;
   //c.v5 = orgC.v5;
-
   c.k8 = 1000;
+
   c.k8 = 10;
-
   c.k8_K7 = 1e6;
+
   c.k8_K7 = 2e5;
 +
  c.tauBbeta4 = 55;
 +
  c.beta4 = 18;
 +
  c.tauRbeta1_tauBbeta4 = 400;
 +
  c.beta1_beta4 = 2;
//  cSol.tauBbeta4 = 180000;
//  cSol.tauBbeta4 = 180000;
//  cSol.tauB = 180;
//  cSol.tauB = 180;
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   //document.getElementById('pArsRRFPtimeGraph').refresh();
   //document.getElementById('pArsRRFPtimeGraph').refresh();
   //document.getElementById('Kostal2004fig3AGraph').refresh();
   //document.getElementById('Kostal2004fig3AGraph').refresh();
-
   //document.getElementById('pSB1A2conGraph').refresh();
+
   document.getElementById('pSB1A2conGraph').refresh();
   //document.getElementById('pArsRRFPconGraph').refresh();
   //document.getElementById('pArsRRFPconGraph').refresh();
}
}
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|{{graph|Team:Groningen/Graphs/Characterization/pArsRRFPtime|id=pArsRRFPtimeGraph}}
|{{graph|Team:Groningen/Graphs/Characterization/pArsRRFPtime|id=pArsRRFPtimeGraph}}
|{{graph|Team:Groningen/Graphs/Characterization/Kostal2004fig3A|id=Kostal2004fig3AGraph}}
|{{graph|Team:Groningen/Graphs/Characterization/Kostal2004fig3A|id=Kostal2004fig3AGraph}}
-
|-
+
|- -->
|{{graph|Team:Groningen/Graphs/Characterization/pSB1A2con|id=pSB1A2conGraph}}
|{{graph|Team:Groningen/Graphs/Characterization/pSB1A2con|id=pSB1A2conGraph}}
-
|{{graph|Team:Groningen/Graphs/Characterization/pArsRRFPcon|id=pArsRRFPconGraph}}-->
+
<!--|{{graph|Team:Groningen/Graphs/Characterization/pArsRRFPcon|id=pArsRRFPconGraph}}-->
|}
|}
<!-- Don't forget to update the refreshGraphs function above! -->
<!-- Don't forget to update the refreshGraphs function above! -->

Revision as of 14:56, 18 October 2009

Igemhomelogo.png

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TODO: Talk about the devices we have and in what way we want to characterize them.

Uptake measurements

Sampling scheme
Time (min)
0 10 20 40 60
As(III)exT(0)
(µM) 		0 x
10 x x x x x
20 x
50 x
100 x

To efficiently look at both time and concentration dependent processes we took samples as in the table on the right. Below we list all results, which have been used for fitting all necessary parameters.

TODO: List results. Take conversion from nmol/mg and mg/ml to µM and Vc/Vs into account.

best cur gradient solved
v5/K5
v5
K5
k8/K7
k8
K7
tauBbetaB
tauB
betaB
tauR
betaRN
tauFbetaF
tauF
betaF
tauKbetaK
tauK
betaK
tauGbeta5
tauG
beta5
ars2T
E

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Retrieved from "http://2009.igem.org/Team:Groningen/Modelling/Characterization"