Difference between revisions of "Team:UIUC Illinois/Description"
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<p>Most promoters in the iGEM registry can be separated into two categories. There are constitutive, or “always on,” promoters of various strengths. There are also inducible promoters, which activate transcription based on environmental factors such as light, pH, or the presence of certain molecules. UIUC_Illinois is characterizing a promoter library that gives more control over gene expression, without the need for inducers. We are isolating a set of e. coli promoters that turn on and off according to the host’s growth phase. For example, one promoter may become active during exponential growth but shut off as growth slows. Another promoter may exhibit little to no activity until stationary phase has been reached. These promoters will be useful tools for any teams wishing to time protein production in vivo, for applications such as metabolic engineering, probiotics, or more.</p> | <p>Most promoters in the iGEM registry can be separated into two categories. There are constitutive, or “always on,” promoters of various strengths. There are also inducible promoters, which activate transcription based on environmental factors such as light, pH, or the presence of certain molecules. UIUC_Illinois is characterizing a promoter library that gives more control over gene expression, without the need for inducers. We are isolating a set of e. coli promoters that turn on and off according to the host’s growth phase. For example, one promoter may become active during exponential growth but shut off as growth slows. Another promoter may exhibit little to no activity until stationary phase has been reached. These promoters will be useful tools for any teams wishing to time protein production in vivo, for applications such as metabolic engineering, probiotics, or more.</p> | ||
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| + | <p>Our promoters were originally sourced from large-scale microarray data which tracked transcription of numerous genes in e. coli over time<sup>1</sup>. By looking for patterns in expression, we were able to sort genes into categories based on when they were most active in the growth cycle. We then designed primers to isolate the promoter and RBS sequence upstream of each gene. Inserting these promoter+RBS combinations upstream of a destabilized GFP gene will allow for verification and characterization of their activity.</p> | ||
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| + | <h3>References</h3> | ||
| + | <p>1. Sangurdekar DP, Srienc F, Khodursky AB. A classification based framework for quantitative description of large-scale microarray data. Genome Biol2006;7(4):R32</p> | ||
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Latest revision as of 02:23, 20 October 2016
Description
Just in Time.
A library of growth phase dependent promoters.
Most promoters in the iGEM registry can be separated into two categories. There are constitutive, or “always on,” promoters of various strengths. There are also inducible promoters, which activate transcription based on environmental factors such as light, pH, or the presence of certain molecules. UIUC_Illinois is characterizing a promoter library that gives more control over gene expression, without the need for inducers. We are isolating a set of e. coli promoters that turn on and off according to the host’s growth phase. For example, one promoter may become active during exponential growth but shut off as growth slows. Another promoter may exhibit little to no activity until stationary phase has been reached. These promoters will be useful tools for any teams wishing to time protein production in vivo, for applications such as metabolic engineering, probiotics, or more.
Our promoters were originally sourced from large-scale microarray data which tracked transcription of numerous genes in e. coli over time1. By looking for patterns in expression, we were able to sort genes into categories based on when they were most active in the growth cycle. We then designed primers to isolate the promoter and RBS sequence upstream of each gene. Inserting these promoter+RBS combinations upstream of a destabilized GFP gene will allow for verification and characterization of their activity.
References
1. Sangurdekar DP, Srienc F, Khodursky AB. A classification based framework for quantitative description of large-scale microarray data. Genome Biol2006;7(4):R32
