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<h1 class="headline">Design</h1> | <h1 class="headline">Design</h1> | ||
− | <p><em>Here you can read about the design principles that helped us structure, organize and execute our project. To read the story | + | <p><em>Here you can read about the design principles that helped us structure, organize and execute our project. To read about the story of how we developed the idea of CATE, go to <a href="https://2017.igem.org/Team:ETH_Zurich/Description">Story of CATE</a>. To skip this story and jump directly to how CATE is designed to treat tumors, see <a href="https://2017.igem.org/Team:ETH_Zurich/Applied_Design">CATE in Action.</a> For details about the circuit behind the functioning, visit our <a href="https://2017.igem.org/Team:ETH_Zurich/Circuit">Circuit page.</a></em></p> |
<section class="first"> | <section class="first"> | ||
<h1>Overview</h1> | <h1>Overview</h1> | ||
− | <p> | + | <p>We structured our work in phases and gradually proceeded through them (Figure 1). The phases apply to theoretical (models) as well as practical (experiments) work. In phase one, we get familiar with the details of the respective subjects. Based on existing data, we designed, ordered and built constructs for experimental procedures and further optimization. In phase two, we tested predictions of the models and generated data to fit their parameters. Optimization of single parts was guided by theoretical work in order to achieve functioning parts. |
</p> | </p> | ||
<p> | <p> | ||
− | We designed the project in a hierarchical bottom-up engineering approach: | + | We designed the project in a hierarchical bottom-up engineering approach: we divided the circuit into its different functions (F<sub>a</sub>-F<sub>e</sub>) and engineered them until they met our criteria.</p> |
<p><b>Circuit Functions:</b></p> | <p><b>Circuit Functions:</b></p> | ||
<ul> | <ul> | ||
− | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fa_Tumor_Sensor">Tumor Sensor</a></li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fa_Tumor_Sensor">F<sub>a</sub>: Tumor Sensor</a></li> |
− | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fb_MRI_Contrast_Agent">MRI Contrast Agent</a></li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fb_MRI_Contrast_Agent">F<sub>b</sub>: MRI Contrast Agent</a></li> |
− | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fc_Anti_Cancer_Toxin">Anti-Cancer Toxin</a></li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fc_Anti_Cancer_Toxin">F<sub>c</sub>: Anti-Cancer Toxin</a></li> |
− | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fd_Heat_Sensor">Heat Sensor</a></li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fd_Heat_Sensor">F<sub>d</sub>: Heat Sensor</a></li> |
− | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fe_Cell_Lysis">Cell Lysis</a></li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Circuit/Fe_Cell_Lysis">F<sub>e</sub>: Cell Lysis</a></li> |
</ul> | </ul> | ||
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<figure class="fig-nonfloat"> | <figure class="fig-nonfloat"> | ||
− | + | <a href="https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--projectdesign.png"> | |
− | src="https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--projectdesign.png"/> | + | <img |
+ | src="https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--projectdesign.png"/></a> | ||
+ | <figcaption>Figure 1. Phases of the project design. Click on the figure to expand.</figcaption> | ||
</figure> | </figure> | ||
+ | |||
+ | <p> | ||
+ | The individual constructs were assembled by various molecular cloning techniques. Subsequently, functions were assessed with reporter genes such as sfGFP and mCherry. Only if they behaved according to our requirements, we coupled different functions. In parallel, we ordered the full genetic circuit of CATE with restriction sites along the critical loci in order to rapidly exchange promotors, ribosome binding sites or coding sequences after we experimentally optimized the parts.</p> | ||
+ | <p>We worked in parallel on the functions of CATE, which is why every function goes through the phases independently. | ||
+ | </section> | ||
+ | |||
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<h1>Phase I: Initial Design</h1> | <h1>Phase I: Initial Design</h1> | ||
<!-- TODO: Add description & links to Experiments showcasing these steps --> | <!-- TODO: Add description & links to Experiments showcasing these steps --> | ||
− | <p>In Phase I we | + | <p>In Phase I we considered previous work in order to design specific DNA sequences. Subsequently, we planned assembly of the parts into test devices (Figure 2). These were then used to develop assays that can be used to characterize the parts <i>in vitro</i>. |
</p> | </p> | ||
− | |||
<figure class="fig-nonfloat"> | <figure class="fig-nonfloat"> | ||
<img alt="Plasmid creation during the CATE project" | <img alt="Plasmid creation during the CATE project" | ||
src="https://static.igem.org/mediawiki/2017/0/0b/T--ETH_Zurich--Project_design_plasmid_creation.png"/> | src="https://static.igem.org/mediawiki/2017/0/0b/T--ETH_Zurich--Project_design_plasmid_creation.png"/> | ||
− | <figcaption>Plasmid | + | <figcaption>Figure 2. Plasmid creation during the CATE project. Click on the figure to expand.</figcaption> |
</figure> | </figure> | ||
− | < | + | <ul> |
+ | <li>For the AND-gate, we <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#phaseI">rationally designed</a> the genetic sequence of the hybrid promoter. To do so, we relied on work done by previous iGEM teams and <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">let our model guide us</a> towards the most efficient design.</li> | ||
+ | <li>We wanted to reach a clear signal in the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/MRI_Contrast_Agent">MRI step</a>. Thus, for the plasmid expressing bacterioferritin, the RBS with the <a href="https://salislab.net/software/">Salis Lab RBS Calculator</a> to reach maximum expression. Plus, silent mutations were introduced to codon-optimize for expression in <span class="bacterium">E. coli</span> Nissle 1917.</li> | ||
+ | </ul> | ||
+ | |||
</section> | </section> | ||
Line 63: | Line 70: | ||
<section> | <section> | ||
<h1>Phase II: Tests and Optimization</h1> | <h1>Phase II: Tests and Optimization</h1> | ||
− | <p>In this phase the assays | + | <p>In this phase the assays have been developed and show us if the function behaves as expected. At this point, we could therefore start to tune the functions. This was achieved by changing the expression levels of proteins with RBS libraries or different designs of a promotor.</p> |
Line 70: | Line 77: | ||
<ul> | <ul> | ||
− | <li> | + | <li>We tested the quorum sensing system to find the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#QS_end_point">trigger point</a>, at which it activates the AND-gate promoter, or the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#AND_gate_wo_QS">dose-response</a> of different AND-gate promoter designs. |
</li> | </li> | ||
− | + | <li>The initial model was fitted to the experimental data and helped us design the next experiment. Read more about how the model was fitted <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">here</a>. | |
− | <li>The initial model was fitted | + | |
</li> | </li> | ||
− | <li> | + | <li>We <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Heat_Sensor#phaseII">optimized</a> the Heat Sensor's RBS to reduce leakiness of the promoter and thus make it possible to control protein E (which is toxic for cells and leads to lysis, rendering even low leakiness levels infeasible). |
</li> | </li> | ||
− | <li>We measured the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/MRI_Contrast_Agent#dose-response"> | + | <li>We measured the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/MRI_Contrast_Agent#dose-response">AHL dose-response</a> of the bacterioferriting regulating promoter to make sure the promoter is actively inducible.</li> |
<li>In the same way, we characterized the azurin producing <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Anti_Cancer_Toxin#phaseII">test device</a>.</li> | <li>In the same way, we characterized the azurin producing <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Anti_Cancer_Toxin#phaseII">test device</a>.</li> | ||
− | <li>We created a <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Cell_Lysis#phaseII">protein E RBS library</a> to find variants | + | <li>We created a <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Cell_Lysis#phaseII">protein E RBS library</a> to find variants able to be regulated by the heat sensor without immediate killing of the cell after transformation.</li> |
− | <li>We | + | <li>We modelled the <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Heat_Sensor">heat diffusion</a> of 3 hours at 45 °C in tumor tissue. This way, we assessed whether such a procedure is acceptable for the healthy tissue surrounding the tumor. We performed this simulation because the heat sensors showed strongest responses to 45 °C and not 42 °C as initially expected.</li> |
− | + | ||
</ul> | </ul> | ||
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</p> | </p> | ||
</section> | </section> | ||
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<section> | <section> | ||
<h1>Phase III: Demonstration of the function</h1> | <h1>Phase III: Demonstration of the function</h1> | ||
− | |||
<p>Important experiments that show our system at work were performed with biological triplicates. The assays were kept the same as in phase II and | <p>Important experiments that show our system at work were performed with biological triplicates. The assays were kept the same as in phase II and | ||
− | <a href="https://2017.igem.org/Team:ETH_Zurich/Protocols">Protocols</a> are available. | + | <a href="https://2017.igem.org/Team:ETH_Zurich/Protocols">Protocols</a> are available. In this phase we show that we managed to get functions working as a result of our engineering efforts.</p> |
+ | <ul> | ||
+ | <li>In an <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/MRI_Contrast_Agent#mri-session">MRI imaging session</a>, we showed that bacterioferritin expressed in our strain indeed leads to a marked decrease in the MRI signal which demonstrates its usability as an MRI contrast agent in vitro and confirms the potential to use it as an in vivo reporter of tumor sensing.</li> | ||
+ | <li>In order to make the thermosensing system tight, we rationally designed an RBS library to tune expression levels of tlpA. By screening for the best variant we were able to dramatically <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Heat_Sensor#phaseII">improve our initial design</a>.</li> | ||
+ | <li>We show that it is possible for our engineered bacteria to grow at 37 °C when transformed with the heat-inducible cell-lysis system. After inducation at 45 °C, we can show that the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Cell_Lysis#phaseIII">cells lyse and release their protein-content into the environment</a>.</li> | ||
+ | </ul> | ||
</section> | </section> | ||
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</main> | </main> | ||
</html> | </html> | ||
{{ETH_Zurich/Footer_N}} | {{ETH_Zurich/Footer_N}} |
Latest revision as of 03:07, 2 November 2017
![CATE logo](https://static.igem.org/mediawiki/2017/d/d6/T--ETH_Zurich--LogoMinimal.png)
Design
Here you can read about the design principles that helped us structure, organize and execute our project. To read about the story of how we developed the idea of CATE, go to Story of CATE. To skip this story and jump directly to how CATE is designed to treat tumors, see CATE in Action. For details about the circuit behind the functioning, visit our Circuit page.
Overview
We structured our work in phases and gradually proceeded through them (Figure 1). The phases apply to theoretical (models) as well as practical (experiments) work. In phase one, we get familiar with the details of the respective subjects. Based on existing data, we designed, ordered and built constructs for experimental procedures and further optimization. In phase two, we tested predictions of the models and generated data to fit their parameters. Optimization of single parts was guided by theoretical work in order to achieve functioning parts.
We designed the project in a hierarchical bottom-up engineering approach: we divided the circuit into its different functions (Fa-Fe) and engineered them until they met our criteria.
Circuit Functions:
![](https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--projectdesign.png)
The individual constructs were assembled by various molecular cloning techniques. Subsequently, functions were assessed with reporter genes such as sfGFP and mCherry. Only if they behaved according to our requirements, we coupled different functions. In parallel, we ordered the full genetic circuit of CATE with restriction sites along the critical loci in order to rapidly exchange promotors, ribosome binding sites or coding sequences after we experimentally optimized the parts.
We worked in parallel on the functions of CATE, which is why every function goes through the phases independently.
Phase I: Initial Design
In Phase I we considered previous work in order to design specific DNA sequences. Subsequently, we planned assembly of the parts into test devices (Figure 2). These were then used to develop assays that can be used to characterize the parts in vitro.
![Plasmid creation during the CATE project](https://static.igem.org/mediawiki/2017/0/0b/T--ETH_Zurich--Project_design_plasmid_creation.png)
- For the AND-gate, we rationally designed the genetic sequence of the hybrid promoter. To do so, we relied on work done by previous iGEM teams and let our model guide us towards the most efficient design.
- We wanted to reach a clear signal in the MRI step. Thus, for the plasmid expressing bacterioferritin, the RBS with the Salis Lab RBS Calculator to reach maximum expression. Plus, silent mutations were introduced to codon-optimize for expression in E. coli Nissle 1917.
Phase II: Tests and Optimization
In this phase the assays have been developed and show us if the function behaves as expected. At this point, we could therefore start to tune the functions. This was achieved by changing the expression levels of proteins with RBS libraries or different designs of a promotor.
- We tested the quorum sensing system to find the trigger point, at which it activates the AND-gate promoter, or the dose-response of different AND-gate promoter designs.
- The initial model was fitted to the experimental data and helped us design the next experiment. Read more about how the model was fitted here.
- We optimized the Heat Sensor's RBS to reduce leakiness of the promoter and thus make it possible to control protein E (which is toxic for cells and leads to lysis, rendering even low leakiness levels infeasible).
- We measured the AHL dose-response of the bacterioferriting regulating promoter to make sure the promoter is actively inducible.
- In the same way, we characterized the azurin producing test device.
- We created a protein E RBS library to find variants able to be regulated by the heat sensor without immediate killing of the cell after transformation.
- We modelled the heat diffusion of 3 hours at 45 °C in tumor tissue. This way, we assessed whether such a procedure is acceptable for the healthy tissue surrounding the tumor. We performed this simulation because the heat sensors showed strongest responses to 45 °C and not 42 °C as initially expected.
Phase III: Demonstration of the function
Important experiments that show our system at work were performed with biological triplicates. The assays were kept the same as in phase II and Protocols are available. In this phase we show that we managed to get functions working as a result of our engineering efforts.
- In an MRI imaging session, we showed that bacterioferritin expressed in our strain indeed leads to a marked decrease in the MRI signal which demonstrates its usability as an MRI contrast agent in vitro and confirms the potential to use it as an in vivo reporter of tumor sensing.
- In order to make the thermosensing system tight, we rationally designed an RBS library to tune expression levels of tlpA. By screening for the best variant we were able to dramatically improve our initial design.
- We show that it is possible for our engineered bacteria to grow at 37 °C when transformed with the heat-inducible cell-lysis system. After inducation at 45 °C, we can show that the cells lyse and release their protein-content into the environment.