Revision as of 20:56, 1 November 2017

Results

We integrated 5 distincts functions in E. coli Nissle to build an autonomous, specific an 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 experiment data and are able to assess its performance in silico

Part designed 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 successful 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 associated with healthy and tumor tissue as well as taking into account the bacterial population density.

Characterized the expression of a genetically encoded MRI contrast agent bacterioferritin in E. coli Nissle 1917.
Participated to the fit of some parameters of our 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.

Developed an assay to characterize killing of cells in a cell line when supplemented with supernatant of lysed bacteria expressing azurin.
Expressed it successfully in our bacteria.
Quantified the potential treatment efficiency of our system.

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.

Showed that we can induce lysis of the bacteria when transformed with protein E under the thermosensitive promoter and induced at 45° C.

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 <p><a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Heat_Sensor">Model</a></p>
-<h2>Cell Lysis</h2>
+<h1>Cell Lysis</h1>
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