Difference between revisions of "Team:ETH Zurich/Model"
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Revision as of 23:17, 18 October 2016
MODEL
OVERVIEW
Figure 1: Schematic view of the model structure. Each module represents a circuit element.
GOALSINTRODUCTION
Due to the complexity of our system, an intuitive design would not be sufficient. Several components must be tuned specifically for our application, and the correct functioning of the candidate designs is not obvious. We developed a hybrid deterministic-stochastic model of our system covering the mechanics of the components we use. The main goals include a model-aided design of the system, tuning of the system components for our application and prediction of the system behavior.
The stochastic approach of our model allows us to exploit the intrinsic noise at our advantage: our model describes the
The model matches the modular sructure of the system, making it easier to exchange components and test their behavior when combined to the full system. Thanks to this feature we were able to quickly test alternative parts like the lactate sensor and the CRISPR switch.
The parameters of the model are strictly based on literature or experimental characterization of the single components. To estimate the parameters from flow cytometry data, we used INSIGHT, a recent method based on Approximate Bayesian Computation (ABC).1
GOALS
PROOF OF CONCEPT
- Identify parameter ranges at which the system shows the desired behavior.
- Predict qualitatively the response of the system to different inputs.
DESIGN INSIGHTS
- Assist in designing the system by comparing the behavior of different design alternatives.
- Tune the system for the desired time scale and concentration ranges.
UNRAVELLING INTEGRASE KINETICS
- Coming soon ...
RESULTS
DESIGN INSIGHTS
We started with exploratory simulations during the design phase and managed to get early insights on the behavior of our system. This helped us in the biological implementation of our system, in particular, the model advised us on the design of the reporter system (add link to the page where you prove this statement), the position of the integrase gene (add link to prove) and the choice of plasmid ORIs (add link to prove).
Moreover we were able to identify potential problems (like leakiness and sensitivity) and predict their influence on the final system. Taking this critical parameters into account, we tried to design and to construct our system such that it works within the required parameter ranges, e.g. low leakiness (add link) and certain ratio between integrase translation and degradation rates (add link).
Figure 2: Simulation of the full system. After exposure to NO and AHL for xxx hours, the system.....
Figure 3: Heatmap of the full system at time = 1 h: For an exposure with 1000 nM AHL and 100 uM NO for 1 h, we observe X-Y% GFP expression.
Figure 4: Heatmap of the full system at time = 2 h: For an exposure with at least 1000 nM AHL and at least 100 uM NO for 2 h, we observe X-Y% GFP expression.
Figure 3:Full system HeatMap 4h
SYSTEM TUNING
Since our application targets specific time and concentration ranges we used the model to tune the components of the system. The sensor module was tuned for the relevant concentrations of quorum sensing molecule and the switch kinetics were adapted for the expected time scale of the measurements.
SYSTEM SIMULATION
After the characterization of the single components we were able to simulate the full system and validate its behavior. This allowed us to model the recorded signal as function of the measured fluorescence distribution, which is a critical operation during actual investigation. The stochastic nature of our model allows us to obtain additional information by taking advantage of the population effect.
Thanks to the sponsors that supported our project:
