ETHZ/Simulation

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(Difference between revisions)
(Simulation of Test Cases)
(Sensitivity Analysis)
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== Sensitivity Analysis ==
== Sensitivity Analysis ==
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For a system like our is is not enough to find a set of parameters for which it 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.
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We were not only interested in finding a set of parameters for which it 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.
The results are plotted in Figure 5.
The results are plotted in Figure 5.
[[Image:ETHZSensitivity.png|center|thumb|Fig. 5: Sensitivity analysis |720px]]
[[Image:ETHZSensitivity.png|center|thumb|Fig. 5: Sensitivity analysis |720px]]

Revision as of 11:46, 15 October 2007

Contents

Protocol

Fig. 1: Flow diagram

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 2000th 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.

For the simulation parameters that have been used please see ADD LINK TO PARAMETER TABLE

Test 1: Learn and recognize IPTG

Fig 1: Test 1: learn and recognize IPTG

Figure 1 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 2: Test 2: learn IPTG and get tested for aTc

Figure 2 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 Figure 1 the system reports by producing red florescence proteins during the recognition phase which matches the desired behavior.

Test 3: Learn and recognize aTc

Fig 3: Test 3: learn and recognize aTc

Figure 3 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 4: Test 4: learn aTc and get tested for IPTG

Figure 4 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 Figure 3 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 it 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.

The results are plotted in Figure 5.

Fig. 5: Sensitivity analysis