Simulation of Promoters behavior using a

Rule-Based model.!

Like we said before the proteins in our regulatory system PpsR, AppA and PrrA belong to acomplex regulatory network and they all work together to control genetic expression and induce all the metabolic changes in Rhodobacter sphaeroides.

We already developed one model based on ODEs, but since this is the first time there has been an attempted to build BioBricks using photosynthetic bacteria, we wanted to try another model to simulate promoters behavior under the same conditions that were tested in the lab and see if they correlated.

Using a rule-based model we were able to evaluate site specific details about protein- protein interaction and for this part we focused mainly on interactions of the domains between PpsR, AppA and PrrA.

The following scheme, Figure 1, shows a complex network, in where our main proteins are involved and the intermediate molecules that help establish the interactions. And the Figure 2, shows the reduced and oxidized form of AppA and PpsR.

The tetramers with intramolecular disulfide bonds (S-S) and thiol groups (SH) denote the oxidized and the reduced form of the PpsR repressor, respectively. The AppA protein has an FAD and a heme cofactor attached where h+ and h–denote the oxidized and reduced form of the heme cofactor (TspO).

Aerobically, the volume of electron flow through the cbb3 oxidase is sufficient to generate an inhibitory signal keeping the PrrBA system inactive and no GFP expression, because the Quinone Pool is maximally oxidized, which is mirrored in the redox state of AppA, keeping PpsR active.

As O2 tensions diminish, the volume of electron flow through the cbb3 oxidase decreases as well and the PrrBA system becomes active through an autophosphorylation of PrrB and transfer of this phosphate group to PrrA.


Reference: and literature, the rest of the constants were assumed second-order rate constants.

Model Construction

Rhodobacter spahaeroides

Rhodopseudomonas palustris


The graphs shows the concentration of our four main proteins in their different sate shuch as, PrrA Active/Inactive, PpsR reduced(-)/oxidize(+) and AP2 (complex formation), all this states in a certain condition (aerobic/darkness, anaerobic/light) over time. Our simulations predict that in aerobic/ darkness condition we can see high concentration of inactive PrrA and in an anaerobic/light condition active PrrA shows high concentration. PpsR it will remain mostly oxidize in aerobic/ darkness and reduced PpsR will be in higher concentration in anaerobic/light, allowing complex formation (AP2).

Overall conclusion about the models.

Well, but we have two almost completely different models, the question is why use two models for the same phenomenon? Well, that's because the perspective of each model, the pros and cons of them. Using two models gives us the opportunity to see two perspectives that help us understand the phenomenon.

On one hand, the differential equations describing protein concentrations and its relationship to oxygen and light, while the rule-based model allows us to observe interactions between molecules, considering agents in agents in a confined space. In other words, the first model allows us to see the system as a whole while the latter sees it as the aggregation of various agents.


Rhodofactory 2012