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- | Abstract
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| + | <h2 class="art-postheader">The project</h2> |
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| + | <p align='justify'>Our work aims at implementing the engineering concept of closed-loop control in <em>E. coli</em>, exploiting quorum sensing. As a proof of concept, we designed a simple genetic controller that regulates the concentration of 3OC6-HSL signalling molecule around a user-defined set-point. The controlled variable (3OC6-HSL) increases as a function of the exogenous anhydroTetracycline input, that triggers LuxI expression. The controller senses the 3OC6-HSL concentration and activates the production of AiiA, that degrades it. |
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- | <p align='justify'>Our work aims at implementing the engineering concept of closed-loop control in E. coli, exploiting quorum sensing. As a proof of concept, we designed a simple genetic controller that regulates the concentration of 3OC6-HSL signalling molecule around a user-defined set-point. The controlled variable (3OC6-HSL) increases as a function of the exogenous anhydroTetracycline input, that triggers LuxI expression. The controller senses the 3OC6-HSL concentration and activates the production of AiiA, that degrades it. | + | |
| To observe the desired behaviour, a fine tuning of the system was necessary. The transcriptional/translational strength of the regulatory elements (promoter+RBS in several combinations) and the enzyme activities were measured and exploited to identify a mathematical model able to predict the behaviour of the controlled system. These predictions made possible an in silico rational fine tuning of the circuit: the most promising modules were selected and assembled into the final circuit, avoiding a cost and time expensive combinatorial approach. | | To observe the desired behaviour, a fine tuning of the system was necessary. The transcriptional/translational strength of the regulatory elements (promoter+RBS in several combinations) and the enzyme activities were measured and exploited to identify a mathematical model able to predict the behaviour of the controlled system. These predictions made possible an in silico rational fine tuning of the circuit: the most promising modules were selected and assembled into the final circuit, avoiding a cost and time expensive combinatorial approach. |
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