Modeling Results

Figure one: In this graph we see the amount of ccdb (the toxin substance)inside the bacteria as a function of time. Both the X axis and the Y axis are in arbitrary units, for a few different values of initial AHL in the cells. Each line stands for a different initial amount of AHL. The one on the top is the graph of AHL(t=0)=2 (again, in arbitrary units) and the bottom one is the graph of AHL(t=0)=6 .
Things to discuss: we can see a peak at the beginning in all the graphs of ccdb, it's because of the time it takes to the tetR to form and repress the ccdb creation . We don't want it to kill the cells. We can solve it by using modelling to determine how much AHL to put. This graphs show an ideal kill switch - there is a great sensitivity to the dependent variable. It shows that our system is holding true to the flexibility promise.

This is a graph of the TetR as a function of time for different values of AHL at time 0. You can see that it's shape is similar to the shape of the results of the experiments.

In the following picture sequence you can see graphs of ccdb as a function of time for different values of the degradation constant of ccdb, on the range of 0.01 to 3.2 A.U.. The shape of the graph for 0.01 A.U. isn't what we want because of the ccdb of the pick not degrading . It tells us that our system functioning is strongly depended on the degradation constant of the toxin protein, as if it doesn't degrade fast enough a high level of ccdb will remain in the cells and kill them slowly . The results of the experiments are similiar to the graph of a low degradation rate. It shows an artifact in our experiment: in the experiments we use ccdb but in the lab we tested our system using RFP, which has different properties, including different degradation constant, than ccdb. This is an example for the importance of modelling. It enables us to use this result to predict problems in the lab and understand better the expirements and their results.