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

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             <li class="outline_title">MODEL</li><!--
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             <li class="outline_title"><a href="#globalWrapper">MODEL</a></li><!--
 
         --><li class="outline_item"><a href="#overview">Overview</a></li><!--
 
         --><li class="outline_item"><a href="#overview">Overview</a></li><!--
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         --><li class="outline_item"><a href="#contributions">Contributions</a></li><!--
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         --><li class="outline_item"><a href="#results">Results</a></li><!--
 
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        --><li class="outline_item"><a href="#structure">Structure</a></li><!--
 
 
         --><li class="outline_item"><a href="#references">References</a></li><!--
 
         --><li class="outline_item"><a href="#references">References</a></li><!--
 
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         <div class="sec white" id="overview">
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         <div class="sec white title_only" id="overview">
 
             <div>
 
             <div>
 
                 <h2>OVERVIEW</h2>
 
                 <h2>OVERVIEW</h2>
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                 <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 known mechanics of the components we use. The main goals include a model-based design of the system, tuning of the system components for our application and prediction of the system behavior.</p>
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                <div class="image_box full_size">
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                    <a href="https://2016.igem.org/File:T--ETH_Zurich--model_structure.svg">
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                        <img src="https://static.igem.org/mediawiki/2016/4/44/T--ETH_Zurich--model_structure.svg">
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                    </a>
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                    <p><b>Figure 1:</b> Schematic view of the model structure. Each module represents a circuit element.</p>
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                </div>
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                <h3 style="padding-top:14px;">QUICK LINKS</h3>
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                <p class="paddingless">These links will quickly take you to the aspects of our work you may be interested in:</p>
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            </div>
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        </div>
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        <div class="sec white three_columns paddingless">
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            <div>
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                <div style="padding-bottom:12px;">
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                    <ul>
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                        <li class="outline_item"><a href="#goals">Goals</a></li>
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                        <li class="outline_item"><a href="#results">Results</a></li>
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                    </ul>
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                </div>
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                <div>
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                    <ul>
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                        <li><a href="https://2016.igem.org/Team:ETH_Zurich/Sensor_Module">Sensor Module</a></li>
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                        <li><a href="https://2016.igem.org/Team:ETH_Zurich/Switch_Module">Switch Module</a></li>
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                    </ul>
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                </div>
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                <div>
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                    <ul>
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                        <li><a href="https://2016.igem.org/Team:ETH_Zurich/Reporter_Module">Reporter Module</a></li>
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                        <li><a href="https://2016.igem.org/Team:ETH_Zurich/Parameters">Parameters</a></li>
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                    </ul>
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                </div>
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            </div>
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        </div>
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        <div class="sec white content_only">
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            <div>
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                <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>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 behavoir, which is much more informative than the average. 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 integrate and tune 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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         <div class="sec light_grey title_only" id="goals">
 
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                 <h2>CONTRIBUTIONS</h2>
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                 <h2>GOALS</h2>
 
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                 <div>
 
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                     <h3>EARLY DESIGN INSIGHTS</h3>
 
                     <h3>EARLY DESIGN INSIGHTS</h3>
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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 reporter system, the placement of the integrase gene and the choice of plasmid ORIs.</p>
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                     <ul>
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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. We designed and constructed the system under awareness of those critical points.</p>
+
                        <li>Prove theoretical feasibility of the circuit.</li>
 +
                        <li>Assist design of the system by simulating qualitative bahavior.</li>
 +
                        <li>Identify potential problems of the system.</li>
 +
                    </ul>
 
                 </div>
 
                 </div>
 
                 <div>
 
                 <div>
 
                     <h3>SYSTEM TUNING</h3>
 
                     <h3>SYSTEM TUNING</h3>
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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. Using 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>
+
                     <ul>
 +
                        <li>Tune the sensors for the for the concentration ranges we want to sense in the gut.</li>
 +
                        <li>Adjust the switch module for the desired time scale, still minimizing false positives.</li>
 +
                    </ul>
 
                 </div>
 
                 </div>
 
                 <div>
 
                 <div>
 
                     <h3>SYSTEM SIMULATION</h3>
 
                     <h3>SYSTEM SIMULATION</h3>
−
                     <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.
+
                     <ul>
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                     </p>
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                        <li>Predict the behavior of the complete system.</li>
 +
                        <li>Predict impact of alternative modules on the behavior of the system.</li>
 +
                        <li>Generate a simple model for inferring the concentrations detected in the gut from fluorescence measurements.</li>
 +
                     </ul>
 
                 </div>         
 
                 </div>         
 
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         <div class="sec white" id="results">
 
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                 <h2>MAIN RESULTS</h2>
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                 <h2>RESULTS</h2>
 
                 <h3>EARLY DESIGN INSIGHTS</h3>
 
                 <h3>EARLY DESIGN INSIGHTS</h3>
−
                <h3>SYSTEM TUNING</h3>
+
                    <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 reporter system, the placement of the integrase gene and the choice of plasmid ORIs.</p>
−
                <p>Bottom-up tuning. 1) Reporters, 2) Switch, 3) Sensors</p>
+
                    <p>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>
−
                <h3>SYSTEM SIMULATION</h3>
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            </div>
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        </div>
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        <div class="sec light_grey" id="structure">
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            <div>
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                <h2>MODEL STRUCTURE</h2>
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                <p>For simplicity and modularity the model is divided in three parts. Those modules are designed to be easily interchangeable. Although here the model is explained in the context of IBD investigation, its structure is general enough to be employed in other associative learning applications.</p>
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                <div class="image_box full_size">
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                     <h3>SYSTEM TUNING</h3>
−
                     <a href="https://2016.igem.org/File:T--ETH_Zurich--model_structure.svg">
+
                     <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>
−
                        <img src="https://static.igem.org/mediawiki/2016/4/44/T--ETH_Zurich--model_structure.svg">
+
−
                    </a>
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                     <p><b>Figure 1:</b> Schematic view of the model structure.</p>
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                </div>
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+
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                <p><i>Figure 1</i> shows a schematic view of the model structure. In order to maximize flexibility we choosed promoter activities as interfaces between modules. Since we tune the system in a bottom-up fashion we explicitely avoided backward dependencies.</p>
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−
 
+
−
                <h3>SENSOR MODULE</h3>
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                <p>The sensor module is composed by an AND gate for the association of the desired events and a regulated promoter for the induction of the output. It is the interface between circuit and external environment and models the activation of the promoters in response to the inputs.</p>
+
  
−
                <p>This component is modeled deterministically as a set of ODEs using leaky Hill functions, since noise is negligible. This is intuitively supported by the high number of molecules involved and the frequency of the binding/unbinding events, which are much faster than gene expression. The hypothesis has been verified by comparing determinitic and stochastic models.
+
                    <h3>SYSTEM SIMULATION</h3>
−
                </p>
+
                    <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.
−
                <h3>SWITCH MODULE</h3>
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                    </p>
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                <h3>REPORTER MODULE</h3>
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Revision as of 10:03, 1 October 2016

PAVLOV'S COLI

MODEL

OVERVIEW

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

QUICK LINKS

These links will quickly take you to the aspects of our work you may be interested in:

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 behavoir, which is much more informative than the average. 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

EARLY DESIGN INSIGHTS

  • Prove theoretical feasibility of the circuit.
  • Assist design of the system by simulating qualitative bahavior.
  • Identify potential problems of the system.

SYSTEM TUNING

  • Tune the sensors for the for the concentration ranges we want to sense in the gut.
  • Adjust the switch module for the desired time scale, still minimizing false positives.

SYSTEM SIMULATION

  • Predict the behavior of the complete system.
  • Predict impact of alternative modules on the behavior of the system.
  • Generate a simple model for inferring the concentrations detected in the gut from fluorescence measurements.

RESULTS

EARLY 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.

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: