Integral Model

The last step in creating a comprehensive model of the contrast mechanism is merging all individual models. Previously the model to predict the FNR concentration in a cell was described. This model will be combined with the decoy sites model, which was extended to contain the specific promotor that was used in this research. Here, the properties of the merged model will be discussed.

Merging the Models

In order to merge the FNR model and the decoy sites model, the exact connection between the two models had to be defined. The FNR model describes the (active) FNR concentration in the bacterial cells. This active FNR species fulfills the role of transcription factor in the decoy sites model. Using this information, the models could simply be combined by merging the Active FNR species of the FNR model and the T (transcription factor) of the decoy sites model. The resulting model schematics are depicted in .

IntegralModelSchematics Schematic of the integral model

Results

This model was implemented as both a ODE and a stochastic model, various parameters were estimated as described on the FNR promotor page. One of the most interesting predictions that can be derived from the integral model is the influence of adding decoy sites to the DNA on the response to oxygen. and endeavour to visualise this prediction as clearly as possible.

DecoySitesOxygen Schematic of the integral model

DecoySitesOxygenResponse Schematic of the integral model

From it can be concluded that the sharpest response is realised using no decoy sites. Looking at it can be seen that adding decoy sites decreases the steady state expression rate, which is undesired. Combining these observations, adding decoy sites to the construct appears to only have negative effects and the idea should based on the modeling be dismissed.

Sensitivity Analysis

A sensitivity analysis is carried out over the integral model. It is aimed to see the effect of uncertainties in different parameters on the result curve. Therefore we know which parameters are crucial control points. In the sensitivity analysis, the parameters are added with a random error and put into the model. The method of Latin hypercube sampling is used to generate the error so that the error distribution is uniform over the region.

References