The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering

McLaughlin, James Alastair; Beal, Jacob; Mısırlı, Göksel; Grünberg, Raik; Bartley, Bryan; Scott-Brown, James; Vaidyanathan, Prashant; Fontanarrosa, Pedro; Oberortner, Ernst; Wipat, Anil; Gorochowski, Thomas E.; Myers, Chris J.

doi:10.3389/fbioe.2020.01009

Cited by 46 publications

(49 citation statements)

References 62 publications

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“…Standardisation has been a key pursuit in the field of synthetic biology, but until recently, has largely been an idealised concept lacking broad implementation. The Synthetic Biology Open Language (SBOL) provides a framework for standardised descriptions of biological part design and function [19], and the Global Biofoundry Alliance [20] aims to share biological automation protocols, projects, and data. Open source projects such as these provide the opportunity to In the future, it is likely that yeast will be engineered to produce complex beverages directly from CO 2 feedstocks.…”

Section: Glossarymentioning

confidence: 99%

Bioinformational trends in grape and wine biotechnology

Dixon

Williams

Pretorius

2022

Trends in Biotechnology

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The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change. The shock and awe of the pandemic's creative destructionThe years of global pandemic outbreaks, 1346, 1918, and 2020, loom large in history because these low-probability, high-impact disasters have devastating impacts on people's lives with tranquilising effects on society as a whole. In this regard, the 2020 coronavirus outbreak was no less damaging than previous once-in-a-century pandemics, global blights (see Glossary), and other catastrophes. This paper explores the future of grape and wine biotechnology with future global biological shocks in mind. These shocks include both the potential for global blights but also the disruptive potential of climate change. It is important to note that these two shocks combine in a layered manner; temperature change combined with existing land use patterns can contribute to the likelihood of a new global blight emerging. COVID-19 exemplifies the power of a global biological shock. The virus has infected millions of people and reset the trajectory of macro political, economic, sociological, technological, legal, and environmental (PESTLE) forces that shape the modern world. Such powerful forces are of course not solely the province of human biological shocks, and global blights harbour similar levels of disruptive potential. This paper explores the emerging science and technologies that might mitigate the risks of a global blight in the grape and wine sector. HighlightsUncertainty caused by pandemics, global blights, and climate change, tends to heighten the focus on how biotechnological advances can mitigate challenges to public safety, cyberbio resilience, environmental sustainability, and bioeconomic security.Convergence of cyberbio technologies and high-throughput automation in biofoundries offers future-shaping bioinformational engineering opportunities with transformational potential to the wine industry.Consumer preferences, biosecurity, and bioethical considerations, must guide research directions and adoption of new bioinformational engineering technologies at the levels of both problem selection and experimental design.

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Section: Glossarymentioning

confidence: 99%

Bioinformational trends in grape and wine biotechnology

Dixon

Williams

Pretorius

2022

Trends in Biotechnology

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“…We aim to complete and automate the DBTL cycle in synthetic biology, proposing a workflow that integrates LOICA, Flapjack [12] and SynBioHub [5] via SBOL3 [4]. In the design stage SBOL/SBML will provide flexibility to use other existing bioCAD tools such as iBioSim [11] as part of the workflow.…”

Section: Future Workmentioning

confidence: 99%

“…The design-build-test-learn (DBTL) cycle is central to engineering disciplines and each phase requires appropriate tools, standards and workflows, which are still in development in synthetic biology. The synthetic biology open language (SBOL) is an open standard for the representation of in silico biological designs that covers the DBTL cycle and has attracted a community of developers that have produced an ecosystem of software tools [4].…”

Section: Introductionmentioning

confidence: 99%

LOICA: Logical Operators for Integrated Cell Algorithms

Vidal

Vidal-Céspedes

Rudge

2021

Preprint

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Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.

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“…Advances in biological data representation in the form of enhancements to SBOL2 for comprehensive representation of the design-build-test-learn cycle [5, 8] and ultimately the development of SBOL3 [11], which in turn enabled us to accelerate the rate of data standard development and integration.…”

Section: Approachmentioning

confidence: 99%

“…• Advances in biological data representation in the form of enhancements to SBOL2 for comprehensive representation of the design-build-test-learn cycle [5,8] and ultimately the development of SBOL3 [11], which in turn enabled us to accelerate the rate of data standard development and integration. • A plugin interface to SynBioHub [12], which enabled rapid development of new functionality for data visualization, submission, and exchange [10].…”

Section: Approachmentioning

confidence: 99%

Data Representation in the DARPA SD2 Program

Roehner

Beal

Bartley

et al. 2021

Preprint

Self Cite

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Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems, but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves. The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing. The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases. This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants. This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration, enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation.

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The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering

Cited by 46 publications

References 62 publications

Bioinformational trends in grape and wine biotechnology

Bioinformational trends in grape and wine biotechnology

LOICA: Logical Operators for Integrated Cell Algorithms

Data Representation in the DARPA SD2 Program

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