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From 2011.igem.org

THE 2011 VCU iGEM PROJECT

        Isoprenoids are a class of extremely diverse naturally occurring compounds that are commonly found in plants. These compounds serve a variety of functions in their host, and have been found to have a number of aromatic, flavor, and pharmaceutical properties. Many increasingly important drugs, such as artemisinin and Taxol are isoprenoids. Isoprenoids are produced in their natural host by a class of enzymes known as terpene synthases. Isoprenoids are classified based on the number of carbons, and fall into three main groups: monoterpenoids (10), sesquiterpenoids (15), and diterpenoids (20). Extracting isoprenoids from their natural source is generally expensive, inefficient, and environmentally burdensome. For this reason, the development of a synthetic biology solution to commercial isoprenoid biosynthesis is a fast-growing area of research. Our project has focused on the synthesis of nerolidol, a sesquiterpenoid found in strawberries, in prokaryotic hosts. Nerolidol is thought to aid in trans-dermal delivery of patch medications.

        Synechococcus elongatus is a model cyanobacterium that we are developing as a platform for metabolic engineering. As a heterotrophic organism, Synechococcus would be an opportune means to minimize input costs and thus maximize output efficiency if developed as a viable biomachine. Because it lacks the extensive genetic characterization such as that of E. coli, it presents a unique set of challenges for synthetic biology. Our work with Synechococcus involves characterizing and modeling promoter strength, developing a viable nerolidol-producing strain, and modeling its circadian rhythm.

        Modeling Synechococcus promoters. Understanding the promoters of the host is imperative for any synthetic biology experiment. A significant part of our research has been to characterize and predict Synechococcus promoter strength through a model based on a position-weighted matrix (PWM). A PWM is a matrix representation of the occurrence of a specific character at a given position. This type of representation can give us insight into the relative frequencies of each of the 4 bases throughout the various motifs of a promoter. A promoter is made of several motifs. At each position in the motifs, trends can be seen in the relative occurrence of each of the 4 bases, as seen in the figure below, adapted from Rhodius and Mutalik 2009, in which they used a PWM to model E. coli promoters.

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To generate our PWM, we used a computational tool called Motif Occurrence Detection Suite (MOODS). We are currently determining the fit of our model to known promoter strengths using a Pearson’s correlation. Because of their simplicity to build and interpret, PWMs are popular in determining transcriptional factor binding sites. PWMs assume and equal additive contribution for each position in the motif. Whether or not this assumption is true for promoters is not yet known. We hope to elucidate this matter as we investigate Synechococcus promoter strengths.