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− | <h1>Environment Sensing</h1> | + | <h1>Environment Sensing: Overview and Goals</h1> |
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+ | As discussed in the <a href="https://2017.igem.org/Team:ETH_Zurich/Description">description of our project</a> 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 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 this dual sensing should help to get off-target synthesis of azurin as low as possible. | ||
+ | </p> | ||
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+ | Our gene design implements the double sensing of cell density and lactate (see FIGURE). We have modeled it to predict its behavior in both in vivo and in vitro situations, depending on relevant genes expression dynamics. | ||
+ | </p> | ||
+ | <div> | ||
+ | </div> | ||
+ | <p> | ||
+ | Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of E. coli Nissle, enabled us to characterize the parameter space where our system would meet both the specificity and Azurin output requirements that we preliminarily set thanks to our Azurin toxicity assays (LINK TO EXPERIMENT) | ||
+ | </p> | ||
+ | <p> | ||
+ | After we gave preliminary guidelines for the first design of our plasmids based on our parameter space search and previously characterized iGEM BioBrick, we explored which experiments could give us the important parameters of our system. Once determined and conducted in the wet lab, we fitted the obtained data to check where our current system was standing. | ||
</p> | </p> | ||
</section> | </section> |
Revision as of 08:39, 2 October 2017
![CATE logo](https://static.igem.org/mediawiki/2017/d/d6/T--ETH_Zurich--LogoMinimal.png)
TOC
Environment Sensing: Overview and Goals
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 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 this dual sensing should help to get off-target synthesis of azurin as low as possible.
Our gene design implements the double sensing of cell density and lactate (see FIGURE). We have modeled it to predict its behavior in both in vivo and in vitro situations, depending on relevant genes expression dynamics.
Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of E. coli Nissle, enabled us to characterize the parameter space where our system would meet both the specificity and Azurin output requirements that we preliminarily set thanks to our Azurin toxicity assays (LINK TO EXPERIMENT)
After we gave preliminary guidelines for the first design of our plasmids based on our parameter space search and previously characterized iGEM BioBrick, we explored which experiments could give us the important parameters of our system. Once determined and conducted in the wet lab, we fitted the obtained data to check where our current system was standing.