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Lee M.E.,University of California at Berkeley | DeLoache W.C.,University of California at Berkeley | Cervantes B.,University of California at Berkeley | Dueber J.E.,University of California at Berkeley | And 2 more authors.
ACS Synthetic Biology

Saccharomyces cerevisiae is an increasingly attractive host for synthetic biology because of its long history in industrial fermentations. However, until recently, most synthetic biology systems have focused on bacteria. While there is a wealth of resources and literature about the biology of yeast, it can be daunting to navigate and extract the tools needed for engineering applications. Here we present a versatile engineering platform for yeast, which contains both a rapid, modular assembly method and a basic set of characterized parts. This platform provides a framework in which to create new designs, as well as data on promoters, terminators, degradation tags, and copy number to inform those designs. Additionally, we describe genome-editing tools for making modifications directly to the yeast chromosomes, which we find preferable to plasmids due to reduced variability in expression. With this toolkit, we strive to simplify the process of engineering yeast by standardizing the physical manipulations and suggesting best practices that together will enable more straightforward translation of materials and data from one group to another. Additionally, by relieving researchers of the burden of technical details, they can focus on higher-level aspects of experimental design. © 2015 American Chemical Society. Source

Kittleson J.T.,University of California at Berkeley | Deloache W.,University of California at Berkeley | Cheng H.-Y.,University of California at Berkeley | Anderson J.C.,University of California at Berkeley | Anderson J.C.,QB3 California Institute for Quantitative Biological Research
ACS Synthetic Biology

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. © 2012 American Chemical Society. Source

Densmore D.,Joint BioEnergy Institute | Densmore D.,University of California at Berkeley | Hsiau T.H.-C.,University of California at Berkeley | Kittleson J.T.,University of California at Berkeley | And 5 more authors.
Nucleic Acids Research

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. © The Author(s) 2010. Published by Oxford University Press. Source

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