Team/CINVESTAV-IPN-UNAM MX/Rule-Based.htm
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+ | <p id="text2">Disscusion.</p> | ||
+ | <p>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). | ||
+ | <br> | ||
<p id="text2">Overall conclusion about the models.</p> | <p id="text2">Overall conclusion about the models.</p> | ||
<p>Well, but we have two almost completely different models, the question is why use two | <p>Well, but we have two almost completely different models, the question is why use two |
Revision as of 03:54, 27 October 2012
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.
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.
Parameters:
Reference: http://bionumbers.hms.harvard.edu/ and literature, the rest of the constants were assumed second-order rate constants.
Model Construction
Rhodobacter spahaeroides
Rhodopseudomonas palustris
Disscusion.
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