Difference between revisions of "Team:KU Leuven/Modeling/Internal"
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Transcription is the first step of gene expression. It involves the binding of RNA polymerase to the promoter region and the formation of mRNA. The transcription rate is dependent on a number of conditions like the promoter strength and the used strain. Our system has both constitutive promoters, which are always active, and inducable promoters, which can be activated or repressed. <br> </p> | Transcription is the first step of gene expression. It involves the binding of RNA polymerase to the promoter region and the formation of mRNA. The transcription rate is dependent on a number of conditions like the promoter strength and the used strain. Our system has both constitutive promoters, which are always active, and inducable promoters, which can be activated or repressed. <br> </p> | ||
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<b> Maximum Transcription rate </b> <br> <p> | <b> Maximum Transcription rate </b> <br> <p> | ||
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These values all depend on measuring activities and since the strains, media and antibiotic markers won’t be completely the same, the real values will diverge from the values found with these simple calculations. Nevertheless, these values can give us an idea about the relative strength of the different promoters. We could gather the real values of mRNA concentration in our cells by executing a qPCR, but for now we will use the found values for our model. The last important parameter for transcription is the copy number in which the genes are present in the cell. The plasmid that incorporates the used genes, has an ORI with a copy number between 100 and 300. In our model we will use a mean copy number of 200. | These values all depend on measuring activities and since the strains, media and antibiotic markers won’t be completely the same, the real values will diverge from the values found with these simple calculations. Nevertheless, these values can give us an idea about the relative strength of the different promoters. We could gather the real values of mRNA concentration in our cells by executing a qPCR, but for now we will use the found values for our model. The last important parameter for transcription is the copy number in which the genes are present in the cell. The plasmid that incorporates the used genes, has an ORI with a copy number between 100 and 300. In our model we will use a mean copy number of 200. | ||
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<b> Inducable promoters </b> <br> | <b> Inducable promoters </b> <br> | ||
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<p> | <p> | ||
For inducable promoters the maximum transcription rate has to be multiplied with a factor to integrate the effect of the transcription factors. Our system includes two types of DNA binding proteins: repressors and activators. These proteins bind certain regions in the promoter region of the DNA. This binding can either repress or activate the promoter activity, affecting transcription rate. The repressors used in our system are the cI protein of the lambda phage and the PenI protein. The activator protein is LuxR, which should be activated by AHL. All proteins first need to form homodimers before being able to bind their target DNA.</p> <br> <p> | For inducable promoters the maximum transcription rate has to be multiplied with a factor to integrate the effect of the transcription factors. Our system includes two types of DNA binding proteins: repressors and activators. These proteins bind certain regions in the promoter region of the DNA. This binding can either repress or activate the promoter activity, affecting transcription rate. The repressors used in our system are the cI protein of the lambda phage and the PenI protein. The activator protein is LuxR, which should be activated by AHL. All proteins first need to form homodimers before being able to bind their target DNA.</p> <br> <p> |
Revision as of 09:22, 17 September 2015
Internal Model
1. Introduction
We can think of many relevant questions in implementing a new circuit: how sensitive is the system, how much will it produce and will it affect the growth? As such, it is important to model the effect of the new circuits on the bacteria. This will be done in the Internal Model. We will use two approaches. First we will use a bottom-up approach. This involves building a detailed kinetic model with rate laws. We will use Simbiology and ODE's to study the sensitivity and dynamic processes inside the cell. This is the bottom-up approach. Afterwards, a top-down model, Flux Balance Analysis (FBA), will be used to study the steady-state values for production flux and growth rate. This part is executed by the iGEM Team of Toulouse as part of a collaboration and can be found here
2. Simbiology and ODE
In the next section we will describe our Simbiology model. Simbiology allows us to calculate systems of ODE's and to visualize the system in a diagram. It also has options to make scans for different parameters, which allows us to study the effect of the specified parameter. We will focus on the production of leucine, Ag43 and AHL in cell A and the changing behavior of cell B due to changing AHL concentration. In this perspective, we will make two models in Simbiology: one for cell A and one for cell B. First we will describe how we made the model and searched for the parameters. Afterwards we check the robustness of the model with a parameter analysis and we do scans to check for the effects of molecular noise.
3. Quest for parameters
We can divide the different processes that are being executed in the cells in 7 classes: transcription, translation, DNA binding, complexation and dimerization, protein production kinetics, degradation and diffusion. We went on to search the necessary parameters and descriptions for each of these categories. To start making our model we have to chose a unit. We choose to use molecules as unit because many constants are expressed in this unit and it allows us to drop the dillution terms connected to cell growth. We will also work with a deterministic model instead of a stochastic model. A stochastic model will show us the molecular noise, but we will check this with parameter scans.
The next step is to make some assumptions:
- The effects of cell division can be neglected
- The substrate pool can not be depleted and the concentration (or amount of molecules) of substrate in the cell is constant
- The exterior of the cell contains no leucine at t=0 and is perfectly mixed
- Diffusion happens independent of cell movement and has a constant rate
4. System
5. Results
Cell A graph of all, graph of Leucine, graph of AHL
Cell B graph of all, graph with induction and without induction
Sensitivity analysis
Conclusion and discussion