NTU@iGEM Project Introduction Methodology Team Introduction Team background Notebook Dry Lab Introduction ODEs used Modeling Constructs Parameters used Deterministic Modeling Sensitivity Analysis Stochastic Modeling Wetlab Introduction Materials and Equipment Protocols Verification of Detection & Lysis System Our New Parts Characterization Introduction Standard Promoters Characterization of pLacI-GFP pLacI-GFP images and clips Characterization of pLsrA-YFP New characterization Proposal Parameter Estimation & Correlations Acknowledgements

Preliminary Run

This model was generated using the program [http://www.cellware.org/index.html CellWare]. The above model was simualted using a Hybrid ODE solver thats uses both deterministic and stochastic solvers. Work is now being carried out to see if this program can be used to build and simulate our systems.

All models below have been created using Cellware. [http://x.amath.unc.edu:16080/BioNetS/ Bionets2] is another option but we would first explore the use of the former program. The outputs are exported to MATLAB for plotting and visualisation.

Stochastic models using Gillispie Algorithm don't seem to work well as they seem to require a lot of computing power. Hybrid Stochastic ODEs seems to work better but the accuracy of the results seems to be sacrificed.

Stochastic Model For E7 + Imm Production

The above image shows the model that was used in Cellware for the 1st System.
The Purple Rectangles represent the DNA.
The Green Rectangles represent the mRNA.
The Pink Diamonds represent the Protein.
The Red Circles represent a certain reaction(Mass Action Reactions only since its a requirement for Stochastic simulations)
The Purple Full Circles represented degradation of a particular substance.

Unlike the previous Simulink deterministic models, Cellware does not allow the user to inject certain variables at stipulated times. Therefore, the model shows inoculation of Lactose at the VERY START of the simulation. This is equivalent to exposing a newly divided cell to an environment of lactose rather than allowing it time to reach a certain steady state for its constitutive proteins. Nevertheless, we hope that the model can still allow us some insights into the system.

Before we actually use the program to run any form of stochastic simulations, it would be prudent to check how its deterministic models would turn out. By varying the amount of Lactose input, a few graphs were obtained and they are as shown.
The model shows that there are differences in the output when the Lactose is changed but they are not exactly significant. However the model still shows very similar behaviour to the model we had obtained using Simulink earlier.

Stochastic Model For Lysis Production

The above diagram shows how we have modeled the Lysis Production system in Cellware. Again the legend is the same as the first E7 production system. We would also like to see how the Deterministic models turn out. The results are as shown.
As we can see, a very similar outcome is observed as compared to the model we had obtained in Simulink. The difference in the Lysis protein output under the conditions that Ai-2 is present alone and when Iron and Ai-2 is present is not very pronounced.

For the Stochastic model, the number of molecules are also a guess and more work has to be done is refining the parameters. Presented here is the initial results for the condition that both Ai-2 and iron ions are present, thereby activating the and gate. The stochastic model results are also compared with the deterministic model.

A total of 10 runs was done to observe the results. As expected, the random nature of the algorithm will give unique results for each run.