From 2007.igem.org

Simulating the EducatETH E. coli System

As has been presented in the Modeling page, we have created a model of our system, that can be described as a finite state machine. In order to examine our system's behavior thoroughly, we have to simulate it through all the difference phases that can be reached. We carry out our simulations based on the protocol presented in Fig. 1

Fig. 1: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are tree phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input.

Simulation of Test Cases

To test for all conditions we run timing simulations for all 4 different branches in the flow diagram of our protocol and plotted the concentrations of the inducers, memory proteins and reporter proteins.

• During the first 1000 minutes of the simulation we did not add any inducers to the system to check for the baseline production of proteins.
• Between 1000 and 2000 minutes we added the inducer that should be learned and let the system reach steady state.
• After the 2000^th minute we added the inducer AHL for memory formation and run the simulation for additional 1000 minutes to reach again steady state.
• Between 3000 and 4000 minutes we finally tested if the system reacts in the desired way by showing the right color.

A more detailed overview about the parameters that we used to simulate our system is given in the section Parameters.

Test 1: Learn and recognize IPTG

Fig. 2: Learn and recognize IPTG

Fig. 2 shows the simulated behavior of the system when IPTG is presented both during the learing phase and the recognition phase. In both the learning and recognition phase the system reports by producing green florescence proteins which matches the desired behavior.

Test 2: Learn IPTG and get tested for aTc

Fig. 3: Learn IPTG and get tested for aTc

Fig. 3 shows the simulated behavior of the system when IPTG is presented only during the learing phase but aTc during the recognition phase. In contrast to Fig. 2 the system reports by producing red florescence proteins during the recognition phase which matches the desired behavior.

Test 3: Learn and recognize aTc

Fig 4: Learn and recognize aTc

Fig. 4 shows the simulated behavior of the system when aTc is presented both during the learing phase and the recognition phase. In both the learning and recognition phase the system reports by producing yellow florescence proteins which matches the desired behavior.

Test 4: Learn aTc and get tested for IPTG

Fig 5: Learn aTc and get tested for IPTG

Fig. 5 shows the simulated behavior of the system when aTc is presented only during the learing phase but IPTG during the recognition phase. In contrast to Fig. 4 the system reports by producing cyan florescence proteins during the recognition phase which matches the desired behavior.

Sensitivity Analysis

We were not only interested in finding a set of parameters for which our system is working. To provide a stable performance also in the presence of noise and parameter missmatch we were interested in analysing the sensitivity of the system to find the most sensitive parameters.

There for we simulated the timing behavior while sweeping the parameters by 5%. In detail we did the following for e.g. test case 1: For each paramter we did the timing simulation like shown in Fig. 2 once while increasing the paramter by 5% and once while decreasing the parameter by 5%. Since in test case 1 we are interested in having a high concentration of GFP during the recognition phase with respect to the base line we divide the steady state GFP concentration at 4000 minutes by the baseline concentration at 1000 minutes. Finally as shown in the following equation we define the sensitivity by subtracting the resulting factor that we get with an increase parameter from the resulting factor that we get by decreasing the parameter.

So if there is no difference in the concentration of the desired output during the recognition phase with respect to baseline when sweeping a parameter by +/-5% the sensitivity of that parameter is equal to zero. Otherwise the sensitivity gives us a measure of how much the concentration changes.

We quantified the sensitivity for all parameters and test cases. The results are plotted in Fig. 5.

Fig. 5: Sensitivity analysis

We found the following results:

• Our system is most sensitve to the parameter a which describes the base production. This is not a surpising result since we depend on having a good switch and our signal to noise or with other words output to baseline production level highly depends on the fact that we can reliably switch off the production of the memory proteins cI and p22cII. For a more detailed analysis of the switching behavior please see ADD LINK TO ANALYSIS!!!
• Other sensitive parameters are those that are directly related to the production and decay of the memory proteins cI and p22cII. This can be explained by the fact that our system prefers symmetry between the parameter sets of cI and p22cII to support good switching behavior where the same concentration of p22cII leads to the same repression of cI than vice versa. We can especially see that those parameters seem to be more sensitive for the test cases 1 and 2 where cI has to be stored inside the memory. The reason for this behavior is that due to the missing symmetry between the parameters for cI and p22cII we currently have a bias inside the system towards the production of p22cII.