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

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                 <h2>OVERVIEW</h2>
 
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                <p>We developed a detailed mechanistic model of our system that describes the behavior of our sensor, switch and reporter components. We present a novel stochastic model for integrase switches, which is crucial to capture the kinetics and cell-to-cell variability of our system’s active learning. The model is structured in modules to enable simple integration and assessment of alternative components. Each module corresponds to an engineered circuit that can be separately validated by experiments.</p>
 +
               
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                <p>The model was critical in choosing between alternative designs and providing a proof of concept. We were able to tune our system thanks to a close interaction between experimentalist and modelers. We identified tunable key parameters using sensitivity analysis and determined optimal parameter ranges. Simulations then guided us to adjust the sensitivity of our sensors to the physiological concentration ranges and to optimize the switch for the expected timescale.</p>
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                     <a href="https://2016.igem.org/File:T--ETH_Zurich--model_structure.svg">
 
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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>
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                <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>
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                 <p>The model matches the modular sructure of the system, making easier to exchange components and test their behavior in the full system. With this feature we were able to quickly test alternative parts like the lactate sensor, that was introduced later during development.</p>
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                 <p>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.</p>
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                 <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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                 <p>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 <i>Approximate Bayesian Computation</i> (ABC).<sup><a href="#cit1" class="cit">1</a></sup></p>
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                     <h3>PROOF OF CONCEPT</h3>
 
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                         <li>Identify parameter ranges at which the system shows the desired behavior.</li>
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                         <li>Provide a proof that the system can work in principle.</li>
 
                         <li>Predict qualitatively the response of the system to different inputs.</li>
 
                         <li>Predict qualitatively the response of the system to different inputs.</li>
 
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                     <h3>DESIGN INSIGHTS</h3>
 
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                     <ul>
 
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                         <li>Assist in designing the system by comparing the behavior of different design alternatives.</li>
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                         <li>Assist design of the system by comparing the qualitative behavior of different design alternatives.</li>
 
                         <li>Tune the system for the desired time scale and concentration ranges.</li>
 
                         <li>Tune the system for the desired time scale and concentration ranges.</li>
 
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                     <h3>UNRAVELLING INTEGRASE KINETICS</h3>
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                     <h3>INTEGRASE MODELING</h3>
 
                     <ul>
 
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                         <li>Coming soon ...</li>
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                         <li>Develop a detailed model capturing the kinetics of our integrase-based switch.</li>
 
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                 <h2>RESULTS</h2>
 
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                <h3>PROOF OF CONCEPT</h3>
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                 <h3>DESIGN INSIGHTS</h3>
 
                 <h3>DESIGN INSIGHTS</h3>
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                 <p>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).</p>
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                 <p>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 the placement of the integrase gene. 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.</p>
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                <p>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).</p>
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                <p>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.</p>
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<a href="https://2016.igem.org/File:T--ETH_Zurich--fullsystDetermSimul.svg">
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<p><b>Figure 2:</b> Simulation of the full system. After exposure to NO and AHL for xxx hours, the system..... </p>
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                            <img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">SWITCH TUNING</a>
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                            <img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">SENSOR TUNING</a>
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<p><b>Figure 3:</b> 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. </p>
 
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<p><b>Figure 4:</b> 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. </p>
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                    <p><b>Figure 3:</b> <b>Left:</b> Functional regions of the parameter space satisfying the requirements of our application. <b>Right:</b> The range of parameters for the switch that allows the system to memorize events correctly.</p>
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<p><b>Figure 5:</b> Heatmap of the full system at time = 4 h: For an exposure with at least 1000 nM AHL and at least 100 uM NO for 4 h, we observe X-Y% GFP expression.</p>
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                <h3>INTEGRASE MODELING</h3>
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                <p>Based on the known mechanics of integrases we developed a novel stochastic model for integrases capturing the mechanics of the switching process. We planned to use the stochastic model for estimating the parameters of the model using the <a href="https://2016.igem.org/Team:ETH_Zurich/Parameters">INSIGHT</a> tool. Unfortunately the flow cytometry data of the recombinase couldn't be collected in time for performing the estimation before the wiki freeze.</p>
  
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                <p>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.</p>
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                    <p><b>Figure 4:</b> Stochastic simulation of the switch. The plots show the evolution of the four possible states of a slipping cassette. In this case 1000 trajectories (colored) were simulated. The mean behavior of the population is shown in black.</p>
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                <p>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.
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
 
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
 
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Revision as of 00:21, 20 October 2016

PAVLOV'S COLI

MODEL

OVERVIEW

We developed a detailed mechanistic model of our system that describes the behavior of our sensor, switch and reporter components. We present a novel stochastic model for integrase switches, which is crucial to capture the kinetics and cell-to-cell variability of our system’s active learning. The model is structured in modules to enable simple integration and assessment of alternative components. Each module corresponds to an engineered circuit that can be separately validated by experiments.

The model was critical in choosing between alternative designs and providing a proof of concept. We were able to tune our system thanks to a close interaction between experimentalist and modelers. We identified tunable key parameters using sensitivity analysis and determined optimal parameter ranges. Simulations then guided us to adjust the sensitivity of our sensors to the physiological concentration ranges and to optimize the switch for the expected timescale.

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 model matches the modular sructure of the system, making easier to exchange components and test their behavior in the full system. With this feature we were able to quickly test alternative parts like the lactate sensor, that was introduced later during development.

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.

INTEGRASE MODELING

  • Develop a detailed model capturing the kinetics of our integrase-based switch.

RESULTS

PROOF OF CONCEPT

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 the placement of the integrase gene. 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.

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.

Figure 3: Left: Functional regions of the parameter space satisfying the requirements of our application. Right: The range of parameters for the switch that allows the system to memorize events correctly.

INTEGRASE MODELING

Based on the known mechanics of integrases we developed a novel stochastic model for integrases capturing the mechanics of the switching process. We planned to use the stochastic model for estimating the parameters of the model using the INSIGHT tool. Unfortunately the flow cytometry data of the recombinase couldn't be collected in time for performing the estimation before the wiki freeze.

Figure 4: Stochastic simulation of the switch. The plots show the evolution of the four possible states of a slipping cassette. In this case 1000 trajectories (colored) were simulated. The mean behavior of the population is shown in black.

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.