Difference between revisions of "Team:ETH Zurich/Model"

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                 <h3>INTRODUCTION</h3>
 
                 <h3>INTRODUCTION</h3>
 
                 <p>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.</p>
 
                 <p>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.</p>
−
                 <p>The stochastic approach of our model allows us to exploit the intrinsic noise at our advantage: our model describes the <it>distribution</it> of the population's behavoir, which is much more informative than the average. We can thus interpret quantitative outputs of the system with greater accuracy.</p>
+
                 <p>The stochastic approach of our model allows us to exploit the intrinsic noise at our advantage: our model describes the <it>distribution</it> of the population's behavior, which is much more informative than the average of the observed population, because we can thus interpret quantitative outputs of the system with greater accuracy.</p>
 
                 <p>The model matches the modular sructure of the system, making easier to exchange components and test their behavior in the full system. Thanks to this feature we were able to quickly test alternative parts like the lactate sensor and the CRISPR switch.</p>
 
                 <p>The model matches the modular sructure of the system, making easier to exchange components and test their behavior in the full system. Thanks to this feature we were able to quickly test alternative parts like the lactate sensor and the CRISPR switch.</p>
 
                 <p>The parameters of the model are strictly based on literature or experimental characterization of the single components. The estiamtion of the parameters uses INSIGHT, a recent method based on <i>Approximate Bayesian Computation</i> (ABC) and flow cytomerty data.<sup><a href="#cit1" class="cit">1</a></sup></p>
 
                 <p>The parameters of the model are strictly based on literature or experimental characterization of the single components. The estiamtion of the parameters uses INSIGHT, a recent method based on <i>Approximate Bayesian Computation</i> (ABC) and flow cytomerty data.<sup><a href="#cit1" class="cit">1</a></sup></p>
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<img src="https://static.igem.org/mediawiki/2016/d/d4/T--ETH_Zurich--heatmapFullSystem2h.svg">
 
<img src="https://static.igem.org/mediawiki/2016/d/d4/T--ETH_Zurich--heatmapFullSystem2h.svg">
 
</a>
 
</a>
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<p><b>Figure 10:</b>Full system HeatMap 2h</p>
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<p><b>Figure 10:</b>Full System HeatMap 2h</p>
 
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</div>
 
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Revision as of 21:37, 18 October 2016

PAVLOV'S COLI

MODEL

OVERVIEW

Figure 1: Schematic view of the model structure. Each module represents a circuit element.

INTRODUCTION

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 distribution of the population's behavior, which is much more informative than the average of the observed population, because we can thus interpret quantitative outputs of the system with greater accuracy.

The model matches the modular sructure of the system, making easier to exchange components and test their behavior in 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. The estiamtion of the parameters uses INSIGHT, a recent method based on Approximate Bayesian Computation (ABC) and flow cytomerty data.1

GOALS

PROOF OF CONCEPT

  • Provide a proof that the system can work in principle.
  • Predict qualitatively the response of the system to different inputs.

DESIGN INSIGHTS

  • Assist design of the system by comparing the qualitative behavior of different design alternatives.
  • Tune the system for the desired time scale and concentration ranges.

UNRAVELLING INTEGRASE KINETICS

  • Coming soon ...

RESULTS

DESIGN INSIGHTS

Thanks to qualitative simulations of the system during the design phase, we managed to get early information about the behavior of the the system. In particular the model advised us on the design of the reporter system, the placement of the integrase gene and the choice of plasmid ORIs.

Moreover we were able to identify potential problems (like leakiness and sensitivity) and predict their influence on the final system. We designed and constructed the system under awareness of those critical points.

Figure 2:Full system Simulation

Figure 10:Full system HeatMap 1h

Figure 10:Full System HeatMap 2h

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.

REFERENCES

  • [1] Lillacci, Gabriele, and Mustafa Khammash. "The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations." Bioinformatics 29.18 (2013): 2311-2319.

Thanks to the sponsors that supported our project: