Difference between revisions of "Team:KU Leuven/Modeling/Internal"
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Revision as of 17:52, 17 September 2015
Internal Model
1. Introduction
We can think of many relevant questions when 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 would 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
After this extensive literature search, we can finally set up our complete system of ODEs for every cell.
5. Results
For cell A we made a simulation with cell A in the ON mode as visuable in figure (x). To do this we had to disable the sensitive cI-repressor. This was done by increasing the value of the degradation rate of the repressor. This will be visuable in our simulation because cI gets to 0 fairly quick. We also see that after a while all the LuxR proteins are bound to AHL. Some values are really big (for example the dimer, AHL, Leucine and LuxI values are really high. This is all possible because we do not account for the metabolic burden that is put on the cell in producing these biomolecules. For studying this effect we used FBA. The most interesting we can learn of this graphs is that the cell works according to our design.
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