Team:NTNU Trondheim/Model

From 2012.igem.org

(Difference between revisions)
Line 7: Line 7:
==Overview==
==Overview==
-
To get a better understanding of the dynamics of the system, we made a model of the system using the Cain software <ref>http://cain.sourceforge.net/ </ref>
+
To get a better understanding of the dynamics of the system, we made a model of the system using the Cain software(1) for stochastic simulations. While stochastic simulations are more computationally demanding than deterministic models based on solving ODE's, they allow for random fluctuations that may have a big impact on the system in a cell(2).
-
for stochastic simulations. While stochastic simulations are more computationally demanding than deterministic models based on solving ODE's, they allow for random fluctuations that may have a big impact on the system in a cell(2).
+
As we want our system to react to two different signals, three promoters were required. One to respond to lactate, one to respond to low oxygen and a third to respond to signal molecules controlled by the two other promoters. The last promoter would then control cell lysis. For lactate sensing, we have adapted the lld promoter of E. coli, the vgb promoter from Vitreoscilla is used for the oxygen while the Lux promoter from Vibrio fischeri were used for lysis control. See respective pages for more details.
As we want our system to react to two different signals, three promoters were required. One to respond to lactate, one to respond to low oxygen and a third to respond to signal molecules controlled by the two other promoters. The last promoter would then control cell lysis. For lactate sensing, we have adapted the lld promoter of E. coli, the vgb promoter from Vitreoscilla is used for the oxygen while the Lux promoter from Vibrio fischeri were used for lysis control. See respective pages for more details.
Line 18: Line 17:
In general, the models seems to be too sensitive with small stimulations giving very large effects. A possible reason is that the values we used for the translation rates are too high, as these are estimates. Another possibility is that the propensity for mRNA decay are more different between deterministic models, as in the references, and stochastic models than expected. This was especially the case for the Lux promoter.
In general, the models seems to be too sensitive with small stimulations giving very large effects. A possible reason is that the values we used for the translation rates are too high, as these are estimates. Another possibility is that the propensity for mRNA decay are more different between deterministic models, as in the references, and stochastic models than expected. This was especially the case for the Lux promoter.
-
<references />
+
==Lld promoter==
 +
In E. coli, the Lld promoter regulates the metabolism of l-lactate(4). Two proteins involved in metabolism as well as the regulatory protein Lldr is induced by the presence of lactate. Lldr in soulution will form dimers that will bind to two sites on the promoter DNA. This will block rna polymerase from binding to the DNA, possibly with a looping structure(4). If l-lactate is present in sufficient quantities, it will bind to the lldr dimer and cause a reconformation of the complex. This activates the promoter. A similar system is found in Corynebacterium Glutamicum(5).
 +
 
 +
We isolated the promoter part of the operon and coupled it to expression of the LuxI protein from V. Ficheri(6), see Lux promoter. As Lldr is already present in E. Coli, it was assumed to have an initial consentration as well as a constant production and degradation. A more realistic system could have included a self regulating feedback system, but this would further complicate the model without necessarily increasing the descriptive accuracy.
 +
 
 +
Binding to the promoter was modeled as lldr in solution forming dimers that could bind to the promoter. Lactate would then bind to the dimer, either in solution or bound to DNA. See equations for details. This gave a quite reasonable behaviour in the presence or absence of lactate, but other paths are also possible. We were not able to find any detailed description of the mechanism.
 +
 
 +
The plots show the production of the LuxI protein as a response to the presence of lactate. The plot on the left shows a system with a quite low concentration of lactate, 10 in the model. Under these conditions, only a few, less than 5, promoters are activated and, as result, the LuxI concentration remains low.The plot on the right shows the response to a 100 times larger lactate concentration. Even though the number of active promoters remains relatively low, the constant production of mRNA leads to a large production of LuxI.

Revision as of 09:45, 7 August 2012

Search and Destroy
Search and Destroy
NTNU IS B.A.C.K.
Bacterial Anti-Cancer-Kamikaze

Model

Contents


Overview

To get a better understanding of the dynamics of the system, we made a model of the system using the Cain software(1) for stochastic simulations. While stochastic simulations are more computationally demanding than deterministic models based on solving ODE's, they allow for random fluctuations that may have a big impact on the system in a cell(2).

As we want our system to react to two different signals, three promoters were required. One to respond to lactate, one to respond to low oxygen and a third to respond to signal molecules controlled by the two other promoters. The last promoter would then control cell lysis. For lactate sensing, we have adapted the lld promoter of E. coli, the vgb promoter from Vitreoscilla is used for the oxygen while the Lux promoter from Vibrio fischeri were used for lysis control. See respective pages for more details.

The model can be divided into three parts; the lld promoter, the vgb promoter and the lux promoter. Each of these systems was first modelled separately. This made it easier to observe the effect of changing individual parameters and make reasonable estimates when experimental data were not available. The final model contained 66 reactions and 46 parameters, many of which were estimates. However, we experienced that working with the model and observing how parameter changes influenced protein expressions gave valuable insight into the system.

Stochastic models, as opposed to deterministic models, describe the presence of species with the number of molecules. For small molecules, in this case oxygen and lactate, this gives very high numbers. For example; a 1 mmol concentration of lactate gives about 400000 molecules in the cell assuming a cell volume of 0.7 mu m^3. This many molecules are computationally demanding to keep track of and the diffusion of these molecules through the cell membrane is relatively fast(3). To simplify, lower, constant consentrations and higher propensity functions were used. A possible problem with this simplification is greater fluctuations than a more realistic model.

In general, the models seems to be too sensitive with small stimulations giving very large effects. A possible reason is that the values we used for the translation rates are too high, as these are estimates. Another possibility is that the propensity for mRNA decay are more different between deterministic models, as in the references, and stochastic models than expected. This was especially the case for the Lux promoter.

Lld promoter

In E. coli, the Lld promoter regulates the metabolism of l-lactate(4). Two proteins involved in metabolism as well as the regulatory protein Lldr is induced by the presence of lactate. Lldr in soulution will form dimers that will bind to two sites on the promoter DNA. This will block rna polymerase from binding to the DNA, possibly with a looping structure(4). If l-lactate is present in sufficient quantities, it will bind to the lldr dimer and cause a reconformation of the complex. This activates the promoter. A similar system is found in Corynebacterium Glutamicum(5).

We isolated the promoter part of the operon and coupled it to expression of the LuxI protein from V. Ficheri(6), see Lux promoter. As Lldr is already present in E. Coli, it was assumed to have an initial consentration as well as a constant production and degradation. A more realistic system could have included a self regulating feedback system, but this would further complicate the model without necessarily increasing the descriptive accuracy.

Binding to the promoter was modeled as lldr in solution forming dimers that could bind to the promoter. Lactate would then bind to the dimer, either in solution or bound to DNA. See equations for details. This gave a quite reasonable behaviour in the presence or absence of lactate, but other paths are also possible. We were not able to find any detailed description of the mechanism.

The plots show the production of the LuxI protein as a response to the presence of lactate. The plot on the left shows a system with a quite low concentration of lactate, 10 in the model. Under these conditions, only a few, less than 5, promoters are activated and, as result, the LuxI concentration remains low.The plot on the right shows the response to a 100 times larger lactate concentration. Even though the number of active promoters remains relatively low, the constant production of mRNA leads to a large production of LuxI.

Retrieved from "http://2012.igem.org/Team:NTNU_Trondheim/Model"