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Revision as of 00:57, 2 November 2017
![CATE logo](https://static.igem.org/mediawiki/2017/d/d6/T--ETH_Zurich--LogoMinimal.png)
Tumor Sensor
GOAL
Tune our bacteria so that it activates the synthesis of Azurin only in the right conditions: High cell density AND High lactate
OVERVIEW
As discussed in the description of our project one main issue of anti-cancer treatments is specificity: deleterious effects induced by these treatments should only target tumor cells, and leave healthy cells unaffected. To avoid such so called “off-target” effects, our synthetic bacterial system comprises of two sensing capabilities: sensing of lactate, and of bacterial cell density (via quorum sensing). Because E. coli Nissle should only form colonies in tumors, which are also areas where the ambient lactate concentration is higher than in healthy tissues, we believe that our dual sensing circuit will help minimize off-target synthesis of azurin.
Our gene design implements the double sensing of cell density and lactate. We have modeled it to predict its behavior in both in vivo and in vitro situations, depending on the relevant gene-expression dynamics.
Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of E. coli Nissle, enabled us to set performance criteria and characterize the parameter space where our system would meet the specificity required to our system
After we gave preliminary guidelines for the first design of our plasmids based on our parameter space search and previously characterized iGEM BioBrick, we used our model to fit the obtained data to characterize the main parameters influencing the behavior of our own system: regarding first the quorum sensing system, and then our self-designed hybrid promoters.
From a more precise and closer to reality simulation of our system, we implement a comprehensive
![](https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--overview_model_sensing.png)
Going further
Now that we have obtained a substantial characterization of our system, and that we now the parameter space in which our system achieve an adequate performance for our application, we can rationally and efficiently fine-tune our system for it to reach an optimal functioning point and accordingly increase its performance.