iGEM 2.0—refoundations for engineering biology

Vilanova, Cristina; Porcar, Manuel

doi:10.1038/nbt.2899

Cited by 67 publications

(63 citation statements)

References 19 publications

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“…AHL/luxR affinity was varied using the wild-type Vibrio fisheri gene and a hypersentive mutant, luxR-G2F 10 . RBS strength responsible for GFP translation was varied in the conservation of the Shine-Dalgarno sequence (BBa_B0034 and BBa_B0033 in the Registry of Standard Biological Parts 11, 12 ). Finally, GFP degradation rate was varied through the addition of the LVA-ssrA degradation tag for clpX-mediated proteolysis 13–16 .…”

Section: Resultsmentioning

confidence: 99%

Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training

et al. 2016

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We report a toolbox for exploring the modular tuning of genetic circuits, which has been specifically optimized for widespread deployment in STEM environments through a combination of bacterial strain engineering and distributable hardware development. The transfer functions of sixteen genetic switches, programmed to express a GFP reporter under the regulation of the (acyl-homoserine lactone) AHL-sensitive luxR transcriptional activator, can be parametrically tuned by adjusting high/low degrees of transcriptional, translational, and post-translational processing. Strains were optimized to facilitate daily large-scale preparation and reliable performance at room temperature in order to eliminate the need for temperature controlled apparatuses, which are both cost-limiting and space-constraining. The custom-designed, automated, and web-enabled fluorescence documentation system allows time-lapse imaging of AHL-induced GFP expression on bacterial plates with real-time remote data access, thereby requiring trainees to only be present for experimental setup. When coupled with mathematical models in agreement with empirical data, this toolbox expands the scalability and scope of reliable synthetic biology experiments for STEM training.

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Section: Resultsmentioning

confidence: 99%

Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training

et al. 2016

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“…Both the potential and limitations of standardization in SB are exemplified by the international Genetically Engineered Machine (iGEM) competition, in which students worldwide present synthetic biology projects based on organisms engineered from a toolbox of BioBricks™. However, Biobricks™ has limitations as universal building blocks in SB (Vilanova and Porcar, ; Valverde et al ., ). Interestingly, these limitations have not been an obstacle for a dense network of synthetic biology enterprises to flourish in the US, while in Europe, the landscape of synthetic biology enterprises is sparse by comparison, even if the main efforts to overcome a paucity of standardization are totally or partially of European origin, such as the development of a Standard European Vector Architecture (SEVA; Silva‐Rocha et al ., ), or the standardized representation of SB designs known as the Synthetic Biology Open Language (SBOL; Galdzicki et al ., ).…”

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confidence: 99%

Synthetic microbiology as a source of new enterprises and job creation: a Mediterranean perspective

Vilanova¹,

Porcar

2018

Microbial Biotechnology

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“…GEC then compiles this specification to all genetic circuit designs that satisfy the specified constraints. In this example, only one design is generated because the default part database provided with GEC is relatively small and must be extended with a dual-regulatory promoter from the iGEM (Smolke 2009;Vilanova and Porcar 2014) Registry (BioBrick K094120) to obtain any solutions. To predict which design will have optimal performance, one must use GEC to compile each design to a biochemical model and simulate each model using GEC or another tool.…”

Section: Examplementioning

confidence: 99%

Design Automation in Synthetic Biology

Appleton

Madsen

Roehner

et al. 2017

Cold Spring Harb Perspect Biol

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Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.S ynthetic biology as a discipline has concerned itself with "forward engineering" living systems. This is a purposefully grand goal and is faced with numerous scientific, engineering, political, and ethical challenges. These engineering challenges include the storage of biological information, the design of complex, interacting biological systems using that information, the physical creation of these systems, and the dissemination of foundational principles as abstractions on which the next technical advances can be made. What should be clear to even the most casual observer is that computers, and the computational capabilities they bring with them, are going to be required if the field is going to succeed going forward. Debates remain regarding the framing of the computational approaches (Andrianantoandro et al. 2006;Lux et al. 2012;Densmore and Bhatia 2014), but there are few who debate the important and increasingly prominent role of computation in the field.Synthetic biology and its associated promise have attracted a wide variety of participants. In particular, computer engineers and computer scientists began developing computational tools for engineering these genetic systems using techniques from their disciplines. Electronic design automation (EDA) showed a clear process by which design formalisms could be built, software tools created, and a larger, separate industry developed (see latticeautomation.com; teselagen.com; deskgen.com; benchling.com). This pursuit in the synthetic biology field has recently been coined bio-design automation (BDA) (Densmore 2012). BDA often uses a "divide and conquer" approach for solving small parts of a larger problem one piece at a time and allows for specialists to tackle specific narrow problems in which they have considerable ex-

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iGEM 2.0—refoundations for engineering biology

Cited by 67 publications

References 19 publications

Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training

Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training

Synthetic microbiology as a source of new enterprises and job creation: a Mediterranean perspective

Design Automation in Synthetic Biology

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