"
Team:Groningen/Modelling/Characterization
From 2009.igem.org
(Difference between revisions)
m (Refactored refreshing of graphs.) |
(More general specification of test data (more than just AsinT is now possible)) |
||
| Line 142: | Line 142: | ||
var experiments = {Meng2004: | var experiments = {Meng2004: | ||
{constants:{Vc:0.0073,Vs:(1.1-0.0073),beta4:0,pro:0,ars2T:0},AsT:10e-6, | {constants:{Vc:0.0073,Vs:(1.1-0.0073),beta4:0,pro:0,ars2T:0},AsT:10e-6, | ||
| - | AsinT:[101.917808219178e-6,394.520547945205e-6,723.287671232877e-6, | + | data:{AsinT:[101.917808219178e-6,394.520547945205e-6,723.287671232877e-6, |
| - | + | 1111.23287671233e-6,1229.58904109589e-6], | |
| - | + | time:[60,600,1200,2400,3600]}}, | |
Singh2008: // We assume 5g/L wet cells were used... (at 1100kg/m^3) | Singh2008: // We assume 5g/L wet cells were used... (at 1100kg/m^3) | ||
{constants:{Vc:(0.004545455),Vs:(1-(0.004545455)),pro:0,ars2T:0},AsT:0.467154987e-6, | {constants:{Vc:(0.004545455),Vs:(1-(0.004545455)),pro:0,ars2T:0},AsT:0.467154987e-6, | ||
| - | AsinT:[10.49961532e-6,16.62049351e-6,19.44416512e-6,23.07460003e-6, | + | data:{AsinT:[10.49961532e-6,16.62049351e-6,19.44416512e-6,23.07460003e-6, |
| - | + | 29.40009211e-6,32.06299566e-6], | |
| - | + | time:[60,300,600,1200,1800,3600] | |
| - | + | /*[1.127*60,4.993*60,9.986*60,20.159*60,30.181*60,60.035*60]*/}}}; | |
var varsToMutate = ['v5_K5','v5','k8_K7','k8','tauBbeta4','beta4','tauR_tauB','beta1_beta4']; | var varsToMutate = ['v5_K5','v5','k8_K7','k8','tauBbeta4','beta4','tauR_tauB','beta1_beta4']; | ||
| Line 177: | Line 177: | ||
// Simulate | // Simulate | ||
x0 = arsenicModelInitialization(nc,e[i].AsT); | x0 = arsenicModelInitialization(nc,e[i].AsT); | ||
| - | xt = simulate(x0,e[i].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 (squares of) errors, divided by the average value | ||
| - | var | + | var curcost = 0, n = 0; |
| - | for(var j in e[i]. | + | for(var xn in e[i].data) { |
| - | + | if (xn=='time') continue; | |
| - | + | var avgv = 0; | |
| - | + | for(var j in e[i].data[xn]) avgv += e[i].data[xn][j]; | |
| + | avgv /= e[i].data[xn].length; | ||
| + | for(var j in xt.timeKey) { | ||
| + | curcost += Math.pow((e[i].data[xn][j]-xt[xn][xt.timeKey[j]])/avgv,2); | ||
| + | n++; | ||
| + | } | ||
} | } | ||
| - | cost += Math.sqrt(curcost/ | + | cost += Math.sqrt(curcost/n); // Compute the square root of the average of the squares (RMS) |
weight++; | weight++; | ||
Revision as of 11:09, 5 October 2009
[http://2009.igem.org/Team:Groningen http://2009.igem.org/wiki/images/f/f1/Igemhomelogo.png]
|
|---|
- Modelling
- DetailedModel
- Characterization
- Downloads
TODO: Talk about the devices we have and in what way we want to characterize them.
Uptake measurements
| 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 | ||||
| tauBbeta4 | ||||
| tauB | ||||
| beta4 | ||||
| tauR | ||||
| beta1 | ||||
| E |
|
|
|
Loading graph...
