HEARTBEAT Fuzzy Modeling

Results

Modeling inducible promoters

Based on prior data analysis of data derived from flow cytometry experiments using inducible synthetic NFkB responsive promoters we developed a fuzzy controller which reflects the behavior of inducible promoters.

By using our model we were able to capture the basic behaviour of an inducible promoter (Fig.1).

Figure 1: Activity of an inducible promoter. Promoter of interest is only active if it is inducible and a stimulation is present.

Once this was accomplished, we aimed for capturing the dependency of the activity of the promoter on more heterogeneous parameters, such as affinity score, position score and presence stimulation (Figs. 2a and 2b). As expected, promoter activity was observed only in the presence of a defined stimulation. Obviously, the affinity score produces a more dynamic, steep gradient in a certain range whereas the position score changes the promoter activity only slightly.

Figure 2a: Activity of an inducible promoter depending on the position score (PosScore)

Figure 2b: Activity of an inducible promoter depending on the affinity score (AfScore)

Finally, we tested the combinatorial dependency of promoter activity on both position and affinity for an inducible promoter upon stimulation (Fig. 3). According to our result, an optimal promoter activity is obtained only in case the promoter sequence harbours a high position as well as a high affinity score. As obtained above, a high affinity score raises the promoter activity to a certain level (and therefore can be considered as a more crucial parameter). A shift towards a high position score, however, is still needed to reach an maximal promoter activation.

Figure 3:

Our final model of an inducible promoters (Figure: NFkB 1...which figure?) shows the relationship between stimuli, type of promoter, i.e. inducible, affinity score and position score. The model predicted a high dependency of the promoter activity on affinity score revealing its crucial role in TF binding and transcriptional activation. dazu: "position score gives the last small jump" to the promoter to be an optimal promoter.

what is the model good for: importantly, our model captures the behaviour of an inducible promoter. we next wondered wheter we would be able to establish the same for an non-inducible promoter

from here: constitutives

later on: we have ... for the inducibles, ... for the constitutives. now: could this be fused(?) for a prediction of combinatorial behaviour of

point out: different promoter design, different experimental measuerment, as well as literature data -> final model?

and then jump to final model.