Difference between revisions of "Team:ETH Zurich/Results"
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<h1>Tumor Sensor</h1> | <h1>Tumor Sensor</h1> | ||
<ul> | <ul> | ||
| − | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#phaseI">Designed parts rationally</a>, according to a preliminary functioning point search thanks to our model.</li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#phaseI">Designed parts rationally</a>, according to a <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space"> preliminary functioning point search</a> thanks to our model.</li> |
| − | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#overview">Designed and realized</a> the most relevant experiments to precisely characterize our quorum-sensing system.</li> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#overview">Designed and realized</a> the most relevant experiments to <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">precisely characterize</a> our quorum-sensing system.</li> |
<li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#phaseI">Designed a hybrid promoter</a> that implements AND-gate logic evaluation of L-lactate and AHL and successfully demonstrated its operation.</li> | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#phaseI">Designed a hybrid promoter</a> that implements AND-gate logic evaluation of L-lactate and AHL and successfully demonstrated its operation.</li> | ||
| − | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#AND_gate_wo_QS">Characterized several versions</a> of this hybrid promoter and <a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting# | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor#AND_gate_wo_QS">Characterized several versions</a> of this hybrid promoter and <a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting#improve">improved the model</a> of the hybrid promoter from the observed experimental behavior.</li> |
| − | <li><a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting#fitand"> | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">Step</a> by <a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting#fitand">step</a> fitting of the most relevant parameters controlling the lactate and bacterial cell density sensing.</li> |
| − | <li>Using our model, we could <a href="https://2017.igem.org/ | + | <li>Using our model, we could <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Silico_Final">choose the best version</a> of AND-gate such that it is capable of distinguishing lactate levels and bacterial population densities associated with healthy and tumor tissue.</li> |
</ul> | </ul> | ||
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<h1>Anti-Cancer Toxin</h1> | <h1>Anti-Cancer Toxin</h1> | ||
<ul> | <ul> | ||
| − | <li>Developed an assay to characterize killing of cells | + | <li><a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Anti_Cancer_Toxin#phaseII">Developed an assay</a> to characterize killing of mammalian cells when supplemented with supernatant of lysed bacteria expressing azurin.</li> |
<li>Expressed it successfully in our bacteria.</li> | <li>Expressed it successfully in our bacteria.</li> | ||
| − | <li> | + | <li>Produced azurin in a controlable manner (via AHL induction).</li> |
| − | + | <li>Produced an azurin-rich bacterial lysate.</li> | |
| − | <li> | + | |
<li>Quantification of azurin in our lysate (corresponding to a 140 µM intracellular concentration of azurin).</li> | <li>Quantification of azurin in our lysate (corresponding to a 140 µM intracellular concentration of azurin).</li> | ||
<li>Quantitative assessment of cytotoxic effect of azurin on p53 positive and negative cancer cell lines.</li> | <li>Quantitative assessment of cytotoxic effect of azurin on p53 positive and negative cancer cell lines.</li> | ||
| + | <li>Quantified the potential <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo#InVivo_Killing"> treatment efficiency</a> of our system.</li> | ||
</ul> | </ul> | ||
Revision as of 03:04, 2 November 2017
Results
Achievements
- We integrated 5 distincts functions in E. coli Nissle to build an autonomous, specific and controllable anti-tumor treatment.
- We designed our parts according to a preliminary modeling phase to optimize from start our system.
- We experimentally and analytically characterized these modules.
- We fitted a comprehensive model with our experimental data and are able to assess its performance in silico.
Tumor Sensor
- Designed parts rationally, according to a preliminary functioning point search thanks to our model.
- Designed and realized the most relevant experiments to precisely characterize our quorum-sensing system.
- Designed a hybrid promoter that implements AND-gate logic evaluation of L-lactate and AHL and successfully demonstrated its operation.
- Characterized several versions of this hybrid promoter and improved the model of the hybrid promoter from the observed experimental behavior.
- Step by step fitting of the most relevant parameters controlling the lactate and bacterial cell density sensing.
- Using our model, we could choose the best version of AND-gate such that it is capable of distinguishing lactate levels and bacterial population densities associated with healthy and tumor tissue.
MRI Contrast Agent
- Characterized the expression of a genetically encoded MRI contrast agent bacterioferritin in E. coli Nissle 1917.
- Used our data to fit parameters of the model.
- Showed that the contrast agent 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.
Anti-Cancer Toxin
- Developed an assay to characterize killing of mammalian cells when supplemented with supernatant of lysed bacteria expressing azurin.
- Expressed it successfully in our bacteria.
- Produced azurin in a controlable manner (via AHL induction).
- Produced an azurin-rich bacterial lysate.
- Quantification of azurin in our lysate (corresponding to a 140 µM intracellular concentration of azurin).
- Quantitative assessment of cytotoxic effect of azurin on p53 positive and negative cancer cell lines.
- Quantified the potential treatment efficiency of our system.
Heat Sensor
- Built and characterized a thermoresponsive system that induces expression of a controlled gene at 45 °C but not at 37 °C.
- Validate with a thermal diffusion model the clinical feasibility of a 45 °C induction in the context of our application.
- Built an RBS library for the Heat Sensor, whereby we reduced leakiness far enough to transform the potent lysis-inducing protein E under its control.
Cell Lysis
- Showed that we can induce lysis of the bacteria when transformed with protein E under the thermosensitive promoter and induced at 45° C.
Human Practices
- Brought synthetic biology and bacterial cancer therapy closer to a broad range of people.
- Learned from various experts to think about aspects of our project that go beyond the lab.
