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<article> | <article> | ||
+ | Analyzing the expression strength of individual Promoter-RBS combinations is quite challenging. The main reasons hindering accurate promoter-RBS characterization, are volatile copy-number changes of the expression plasmid (QUELLE) or growth phase specific expression changes (QUELLE). To avoid these errors, we designed a measurement vector carrying two reporter genes, which enables us to normalize the expression strength of the measured promoter-RBS combination to the relative abundance of the vector. | ||
+ | Our measurement vector is based on the expression strength of the different promoter-RBS combinations from our library (BBa_), cloned in front of mRFP (BBa_) and a double terminator (BBa_) inside the pSB1C3 restriction site. Furthermore, our measurement vector carries a eCFP (BBa_) under control of a strong/weak Anderson promoter (BBa_) and RBS (BBa_) combination and a double terminator (BBa_) in the plasmid’s backbone (Fig. PLASMIDKARTE). | ||
+ | The constitutive eCFP expression is proportional to the plasmid’s copy-number. | ||
+ | This enables normalization of the mRFP expression to the plasmid’s copy-number and direct assessment of our library’s promoter-RBS combinations expression strength. As this measurement is independent of plasmid effects it enables comparison with our modeling as well as with other expression constructs. | ||
</article> | </article> | ||
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<article> | <article> | ||
+ | For modeling of our promoter-RBS combinations we used the given strength of the Anderson promoters (BBa_XXX to BBa_YYY) and the strength of different RBS (BBa_AAA, BBa_BBB) to determine an estimate for their absolute strength. | ||
+ | Prior to the experiments, we modeled the expression strength of different promoter and RBS combinations to create a database for our experiments. Therefore we used the given strength of the Anderson promoters and the strength of the different known RBS to determine and visualize their absolute strength shown in Fig.: ?. | ||
+ | When generating these results, we do not only wanted to consider the use of different Anderson promoters, but also analyze the expression strength of different promoters in combinations with different RBS. Especially for our siRNA system, it was interesting to see the difference between inducible and constitutive promoters. | ||
+ | In addition, we modeled other promoters of the parts registry. | ||
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+ | </article> | ||
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+ | <h2>Characterization</h2> | ||
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+ | <article> | ||
+ | After cloning all parts were checked by Sanger sequencing. | ||
+ | The correct plasmids were transformed in E. coli DH5α and grown in LB media. Over-night cultures were diluted to an OD600 of 0.1 and incubated at 37°C and Y rpm for X hours. Afterwards the fluorescence strength of mRFP and eCFP were measured using the Tecan Reader with excitation wavelengths of 558 nm and 435 nm and emission detected at 608 nm and 485 nm. | ||
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+ | </article> | ||
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+ | <h2>Results</h2> | ||
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+ | <article> | ||
+ | In addition to the analyzed expression strengths we also tested the influence of gene expression on the bacteria growth by detection of the OD600. The experiment was performed over 14 hours, by 37 °C and X rpm. | ||
+ | To analyze the production of the substrate we also used real-time PCR for some constructs to determine the total number of transkripts. | ||
+ | We visualized the results in Fig. ?. | ||
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+ | </article> | ||
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+ | <h2>Outlook</h2> | ||
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+ | <article> | ||
+ | With this database we can predict the right Promoter and RBS for every system. We have weak combinations for proteins which are slightly toxic for the cells or cause stress responses and we have powerful combinations for anti-toxicity and membrane proteins. | ||
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+ | </article> | ||
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<figure role="group"> | <figure role="group"> |
Revision as of 20:05, 16 October 2018
Part Collection
Short Summary
Design
Modeling
Characterization
Results
Outlook
Molecular graphics and analyses performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.