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Team:Calgary/Modelling/MC/Results
From 2009.igem.org
UNIVERSITY OF CALGARY
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MODELLING INDEX
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A TOUR OF THE UNIVERSITY OF CALGARY iGEM TEAM
We've reached modelling, the fifth stop on our tour! We've looked in to two different methods of modelling our system: Differential Equation Based Modelling and Membrane Computing. Here, you can explore the similarities and differences, as well as the functions of each method. As well, you can find the results of our characterization of the signalling pathway. Once you're done, we'll move on to the Second Life component of the project HERE. |
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MEMBRANE COMPUTING MODELLING RESULTS
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For complete results, please review our paper (A Model of the Quorum Sensing System in Genetically Engineered E.Coli Using Membrane Computing). 
Distribution of AI-2 Molecules between newborn cells from parent cells. On X-axis, time steps are aligned, and on Y-axis number of AI-2 molecules are placed. This graph shows that the simulation is ran for 3000 time steps and started with one cell (blue line) and other cells are generated over time by divisions (red, green, and yellow lines). This graph demonstrates that number of AI-2 molecules changes logarithmically between divisions, and suddenly drops at each division. 
As concentration of AI2 is high in the environment, the LuxPQ complex detects the signal and switches off the production of GFP proteins. As seen in the figure, at the beginning of the simulation there was a massive production of GFP protein however as time evolves this production decreases and existing GFPs degrades. 
Comparison of production and degradation of GFP proteins in parent cells and newborn cells. The First graph shows that the number of AI-2 molecules in the environment is continuously increasing over the 7000 steps. The second graph shows the number of GFP proteins within one of the parent cells (indicated by ”P”) in the simulation. This cell keeps producing GFP proteins up to the division point. After that the newborn daughter cell (indicated by ”D”) continues the production of GFPs, however it does not last very long as this cell reaches to the point (indicated by red line) that recognizes the high concentration of AI-2 in the environment and cancel the production of GFPs. After this point, the degradations of GFPs occur. Graph 3 demonstrates the number of GFP proteins in one of the newborn cells in the simulation and implies that the production of GFP proteins occurs once (indicated by black line) in this cell. However after very soon the cell changes its biological cascade and hence the inherited GFP proteins and the produced one are degraded. The reason for observing this behavior is since the concentration of AI-2 molecules in the environment is high by the birth time of daughter cells, they switches to the second biological cascade faster than their parent cells that keep producing GFPs for more than 2000 time steps. 
AI-2 Binding to the LuxP-LuxQ Protein Complex. Each column represents one of twenty E.coli bacteria. The state of the modelled bacteria over a period of 50 simulated time steps is depicted along the vertical axis. The color of each cell indicates the binding degree between AI-2 and the LuxP-Q complex. The color spectrum spans from red (no binding) over white to blue (complete binding). As time progresses an increasing amount of AI-2 is produced by LuxS and gets bound to the LuxPQ complex. 
Distributions of Applied Rules. For each rule r0 to r16 the number of its application is charted. Charts like this help to understand which rules, i.e. which interactions, are more or less important within the simulated system or how changes in the rates of reactions affect the rule distributions. 
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