Team:XMU-China/Data

From 2011.igem.org

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== '''Project Name''' ==
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i-''ccdB'': intelligent Control of Cell Density in Bacteria
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== Project Abstract==
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We have developed a series of devices which program a bacteria population to maintain at different cell densities. We have designed and characterized the genetic circuit to establish a bacterial ‘population-control’ device in ''E. coli'' based on the well-known quorum-sensing system from ''Vibrio fischeri'', which autonomously regulates the density of an ''E. coli'' population. The cell density however is influenced by the expression levels of a killer gene (''ccdB'') in our device. As such, we have successfully controlled the expression levels of ''ccdB'' by site-directed mutagenesis of a ''luxR'' promoter (''lux pr'') and error-prone PCR of gene ''luxR'', and finally we have built a database for a series of mutation sites corresponding to different cell densities. An artificial neural network has then been built to model and predict the cell density of an ''E. coli'' population. This work can serve as a foundation for future advances involving fermentation industry and information processing.
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=== Backgrounds ===
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=== Project Detail===
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== Results ==

Revision as of 05:58, 2 September 2011


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Contents

Project Name

i-ccdB: intelligent Control of Cell Density in Bacteria

Project Abstract

We have developed a series of devices which program a bacteria population to maintain at different cell densities. We have designed and characterized the genetic circuit to establish a bacterial ‘population-control’ device in E. coli based on the well-known quorum-sensing system from Vibrio fischeri, which autonomously regulates the density of an E. coli population. The cell density however is influenced by the expression levels of a killer gene (ccdB) in our device. As such, we have successfully controlled the expression levels of ccdB by site-directed mutagenesis of a luxR promoter (lux pr) and error-prone PCR of gene luxR, and finally we have built a database for a series of mutation sites corresponding to different cell densities. An artificial neural network has then been built to model and predict the cell density of an E. coli population. This work can serve as a foundation for future advances involving fermentation industry and information processing.


Backgrounds

Project Detail

Results