• Synthetic Biology


• Regulatory Systems
• Lambda Phage
• Gifsy Phages


• The Switch
• Biological Switches
• Bistable Switches
• Design of our Bi[o]stable Switch
• Step-wise Engineering of the Switch
• Applications


• Synthetic Promoter Library


• Modeling
  • Modeling Approach
  • Modeling SPL
  • Modeling Anti-Repressors

Synthetic Promoter Library

Modulation of gene expression such as of cellular enzyme activities[1] as well as regulation of transcription are amongst some of the areas where Synthetic Promoter Libraries (SPLs) are currently being used. SPL provides an alternative method for gene regulation compared to the old methods, namely those of gene knockout as well as strong over expression, these two usually executed on the basis of apparent rate limiting steps[1].
When working with gene regulation, it is important to elucidate where expression levels are optimal of the given gene you are working with. Under these specifications it is essential to be able to have slight increments in expressional strength when attempting to optimize your gene.
This can be achieved by the usage of an SPL, where the variability in strengths can be achieved by either randomizing the spacer sequences, namely the 17 bases that reside between the -35 and -10 consensus regions, and or some of the bases within the consensus regions, being the -35 and -10 regions.

Figure 1: Illustration of SPL.

The spacer sequences that surround the consensus regions contribute significantly to the strengths of promoters[1]. In our design, we decided to both randomize the spacer sequences as well as randomize 2 bases in both of the consensus regions as seen in the provided diagram. N stands for 25% each of A, C, G and T, while R stands for 50% each of A and G, and W stands for 50% A and T. The point of randomizing both would be to obtain a promoter library that is not biased towards being all strong, by giving 2 bases within each of the consensus regions a 50% chance of being their original bases only 1/16 of all promoters will be strong, this being without taking into consideration the fraction of strong promoters obtainable from the randomized spacer sequences.
As previous studies indicate consensus regions outside of the -35 and -10 regions seem to contribute very little if anything at all in terms of altering promoter strengths, rather the spacer sequences surrounding the -35 and -10 regions seem to have the most significance[2]. This might be due to the three-dimensional structure that forms from the sequences that are arranged from the randomized spacer sequences[2].