BackgroundStandard biological parts, such as BioBricks™ parts, provide the foundation for a new engineering discipline that enables the design and construction of synthetic biological systems with a variety of applications in bioenergy, new materials, therapeutics, and environmental remediation. Although the original BioBricks™ assembly standard has found widespread use, it has several shortcomings that limit its range of potential applications. In particular, the system is not suitable for the construction of protein fusions due to an unfavorable scar sequence that encodes an in-frame stop codon.ResultsHere, we present a similar but new composition standard, called BglBricks, that addresses the scar translation issue associated with the original standard. The new system employs BglII and BamHI restriction enzymes, robust cutters with an extensive history of use, and results in a 6-nucleotide scar sequence encoding glycine-serine, an innocuous peptide linker in most protein fusion applications. We demonstrate the utility of the new standard in three distinct applications, including the construction of constitutively active gene expression devices with a wide range of expression profiles, the construction of chimeric, multi-domain protein fusions, and the targeted integration of functional DNA sequences into specific loci of the E. coli genome.ConclusionsThe BglBrick standard provides a new, more flexible platform from which to generate standard biological parts and automate DNA assembly. Work on BglBrick assembly reactions, as well as on the development of automation and bioinformatics tools, is currently underway. These tools will provide a foundation from which to transform genetic engineering from a technically intensive art into a purely design-based discipline.

The models and simulation tools available to design functionally complex synthetic biological devices are very limited. We formulated a design-driven approach that used mechanistic modeling and kinetic RNA folding simulations to engineer RNA-regulated genetic devices that control gene expression. Ribozyme and metabolite-controlled, aptazyme-regulated expression devices with quantitatively predictable functions were assembled from components characterized in vitro, in vivo, and in silico. The models and design strategy were verified by constructing 28 Escherichia coli expression devices that gave excellent quantitative agreement between the predicted and measured gene expression levels (r = 0.94). These technologies were applied to engineer RNA-regulated controls in metabolic pathways. More broadly, we provide a framework for studying RNA functions and illustrate the potential for the use of biochemical and biophysical modeling to develop biological design methods.

The ability to generate RNA aptamers for synthetic biology using in vitro selection depends on the informational complexity (IC) needed to specify functional structures that bind target ligands with desired affinities in physiological concentrations of magnesium. We investigate how selection for high-affinity aptamers is constrained by chemical properties of the ligand and the need to bind in low magnesium. We select two sets of RNA aptamers that bind planar ligands with dissociation constants (Kds) ranging from 65 nM to 100 μM in physiological buffer conditions. Aptamers selected to bind the non-proteinogenic amino acid, p-amino phenylalanine (pAF), are larger and more informationally complex (i.e., rarer in a pool of random sequences) than aptamers selected to bind a larger fluorescent dye, tetramethylrhodamine (TMR). Interestingly, tighter binding aptamers show less dependence on magnesium than weaker-binding aptamers. Thus, selection for high-affinity binding may automatically lead to structures that are functional in physiological conditions (1–2.5 mM Mg2+). We hypothesize that selection for high-affinity binding in physiological conditions is primarily constrained by ligand characteristics such as molecular weight (MW) and the number of rotatable bonds. We suggest that it may be possible to estimate aptamer–ligand affinities and predict whether a particular aptamer-based design goal is achievable before performing the selection.