Algorithms for automated DNA assembly.

Generating a defined set of genetic constructs within a large combinatorial space provides a powerful method for engineering novel biological functions. However, the process of assembling more than a few specific DNA sequences can be costly, time consuming and error prone. Even if a correct theoretical construction scheme is developed manually, it is likely to be suboptimal by any number of cost metrics. Modular, robust and formal approaches are needed for exploring these vast design spaces. By automating the design of DNA fabrication schemes using computational algorithms, we can eliminate human error while reducing redundant operations, thus minimizing the time and cost required for conducting biological engineering experiments. Here, we provide algorithms that optimize the simultaneous assembly of a collection of related DNA sequences. We compare our algorithms to an exhaustive search on a small synthetic dataset and our results show that our algorithms can quickly find an optimal solution. Comparison with random search approaches on two real-world datasets show that our algorithms can also quickly find lower-cost solutions for large datasets.

Successes and failures in modular genetic engineering.

Synthetic biology relies on engineering concepts such as abstraction, standardization, and decoupling to develop systems that address environmental, clinical, and industrial needs. Recent advances in applying modular design to system development have enabled creation of increasingly complex systems. However, several challenges to module and system development remain, including syntactic errors, semantic errors, parameter mismatches, contextual sensitivity, noise and evolution, and load and stress. To combat these challenges, researchers should develop a framework for describing and reasoning about biological information, design systems with modularity in mind, and investigate how to predictively describe the diverse sources and consequences of metabolic load and stress.

Scalable plasmid transfer using engineered P1-based phagemids.

Dramatic improvements to computational, robotic, and biological tools have enabled genetic engineers to conduct increasingly sophisticated experiments. Further development of biological tools offers a route to bypass complex or expensive mechanical operations, thereby reducing the time and cost of highly parallelized experiments. Here, we engineer a system based on bacteriophage P1 to transfer DNA from one E. coli cell to another, bypassing the need for intermediate DNA isolation (e.g., minipreps). To initiate plasmid transfer, we refactored a native phage element into a DNA module capable of heterologously inducing phage lysis. After incorporating known cis-acting elements, we identified a novel cis-acting element that further improves transduction efficiency, exemplifying the ability of synthetic systems to offer insight into native ones. The system transfers DNAs up to 25 kilobases, the maximum assayed size, and operates well at microliter volumes, enabling manipulation of most routinely used DNAs. The system's large DNA capacity and physical coupling of phage particles to phagemid DNA suggest applicability to biosynthetic pathway evolution, functional proteomics, and ultimately, diverse molecular biology operations including DNA fabrication.

Rapid optimization of gene dosage in E. coli using DIAL strains.

BACKGROUND: Engineers frequently vary design parameters to optimize the behaviour of a system. However, synthetic biologists lack the tools to rapidly explore a critical design parameter, gene expression level, and have no means of systematically varying the dosage of an entire genetic circuit. As a step toward overcoming this shortfall, we have developed a technology that enables the same plasmid to be maintained at different copy numbers in a set of closely related cells. This provides a rapid method for exploring gene or cassette dosage effects. RESULTS: We engineered two sets of strains to constitutively provide a trans-acting replication factor, either Pi of the R6K plasmid or RepA of the ColE2 plasmid, at different doses. Each DIAL (different allele) strain supports the replication of a corresponding plasmid at a constant level between 1 and 250 copies per cell. The plasmids exhibit cell-to-cell variability comparable to other popular replicons, but with improved stability. Since the origins are orthogonal, both replication factors can be incorporated into the same cell. We demonstrate the utility of these strains by rapidly assessing the optimal expression level of a model biosynthetic pathway for violecein. CONCLUSIONS: The DIAL strains can rapidly optimize single gene expression levels, help balance expression of functionally coupled genetic elements, improve investigation of gene and circuit dosage effects, and enable faster development of metabolic pathways.

Periplasmic metal-resistance protein CusF exhibits high affinity and specificity for both CuI and AgI.

The periplasmic protein CusF, as a part of the CusCFBA efflux complex, plays a role in resistance to elevated levels of copper and silver in Escherichia coli. Although homologues have been identified in other Gram-negative bacteria, the substrate of CusF and its precise role in metal resistance have not been described. Here, isothermal titration calorimetry (ITC) was used to demonstrate that CusF binds with high affinity to both Cu(I) and Ag(I) but not Cu(II). The affinity of CusF for Ag(I) was higher than that for Cu(I), which could reflect more efficient detoxification of Ag(I) given the lack of a cellular need for Ag(I). The chemical shifts in the nuclear magnetic resonance (NMR) spectra of CusF-Ag(I) as compared to apo-CusF show that the region of CusF most affected by Ag(I) binding encompasses three absolutely conserved residues: H36, M47, and M49. This suggests that these residues may play a role in Ag(I) coordination. The NMR spectra of CusF in the presence of Cu(II) do not indicate specific binding, which is in agreement with the ITC data. We conclude that Cu(I) and Ag(I) are the likely physiological substrates.