Synthetic Biology Open Language (SBOL) Version 2.2.0

Cox, Robert Sidney; Madsen, Curtis; McLaughlin, James Alastair; Nguyen, Tramy; Roehner, Nicholas; Bartley, Bryan; Beal, Jacob; Bissell, M.G.; Choi, Kiri; Clancy, Kevin; Grünberg, Raik; Macklin, Chris; Mısırlı, Göksel; Oberortner, Ernst; Pocock, Matthew; Samineni, Meher; Zhang, Michael; Zhang, Zhen; Zundel, Zach; Gennari, John H.; Myers, Chris J.; Sauro, Herbert M.; Wipat, Anil

doi:10.1515/jib-2018-0001

Cited by 27 publications

(32 citation statements)

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“…This will allow users to implement DNA-BOT relying on low-cost Opentrons hardware only 12 . Next, we will continue to develop the DNA-BOT software package to integrate with open-source DNA design tools like SBOL [16][17][18] improving UX-design. Currently, 2 comprehensive BASIC linker sets are available ready to use on Opentrons in 96-well plates from Biolegio and more BASIC parts will be made available, enriching the design opportunities for new BASIC and DNA-BOT users 7,14 .…”

Section: Duringmentioning

confidence: 99%

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

Storch

Haines

Baldwin

2019

Preprint

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Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems that perform the desired functions. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process has received serious attention in recent years. Importantly, automating DNA assembly enables larger builds using less researcher time, increasing the accessible design space.However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT).For this purpose, we developed an open-source software package dnabot (https://github.com/BASIC-DNA-ASSEMBLY/dnabot). We demonstrate the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, exploring the promoter, ribosome binding site (RBS) and gene order design space for a 3-gene operon. All 88 constructs were assembled with high accuracy, at a cost of $1.50 -$5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT making it accessible for most labs and democratising automated DNA assembly.

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Section: Duringmentioning

confidence: 99%

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

Storch

Haines

Baldwin

2019

Preprint

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“…One way to systematically learn better models is to identify the minimum set of changes that must be made to the model to align its predictions with observed behaviors of the synthetic genome. To realize these models, we should develop new experimental methods for better characterizing the relationship between genotype and phenotype, new tools discovering and aggregating the data needed for modeling (building on foundations such as the workflow model introduced in SBOL 2.2 [45], and ontology resources such as OBO [68] and the Experimental Factor Ontology [69]), new formalisms for modeling and simulating the combinatorial complexity of biology and the multiple scales between genomes and organismal behavior, new methods for high-performance simulation of large models, new tools verifying models, new frameworks for collaboratively building models, and new representations for the semantic meaning and provenance of models [70,71]. To automatically learn these models from test data, we should develop new repositories of models of individual biological parts that can be composed into models [72]; new methods for generating variants of models that explain new observations by incorporating models of additional parts, alternative kinetic laws, or alternative parameter values; and develop new model selection techniques for non-linear multiscale models [73].…”

Section: Learning Systematically From Test Resultsmentioning

confidence: 99%

“…GFF has already been used in the Sc 2.0 genome engineering project [18]. The other is the Synthetic Biology Open Language (SBOL) [45]. Like GFF, SBOL supports hierarchical description with standardized vocabularies.…”

Section: Rationally Refactoring and Designing Gigabase Genomesmentioning

confidence: 99%

“…All this information must be expressed in the inputs provided from the design stage, and while FASTA or GenBank may be sufficient for a simple sequence, more sophisticated representations, such as GFF or SBOL [29], are needed for hierarchical construction. SBOL can also convey functional information, allowing more manufacturing flexibility in the precise sequence chosen for construction, as well as full details of assembly plans and records [45], such as JGI manages with its BOOST software [53]. Sequence modifications may be expressed in an enhanced sequence encoding language, such as BpForms [58].…”

Section: Building Engineered Genomesmentioning

confidence: 99%

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Organizing genome engineering for the gigabase scale

et al. 2020

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Engineering the entire genome of an organism enables large-scale changes in organization, function, and external interactions, with significant implications for industry, medicine, and the environment. Improvements to DNA synthesis and organism engineering are already enabling substantial changes to organisms with megabase genomes, such as Escherichia coli and Saccharomyces cerevisiae. Simultaneously, recent advances in genome-scale modeling are increasingly informing the design of metabolic networks. However, major challenges remain for integrating these and other relevant technologies into workflows that can scale to the engineering of gigabase genomes.In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will likely be required for gigabase genome engineering. We recommend that the community address these challenges by 1) adopting and extending existing standards and technologies for representing and exchanging information at the gigabase genomic scale, 2) developing new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing new legal and contractual infrastructure to better enable collaboration across multiple institutions.

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“…Synthetic Biology is enabling the formalization of genetic circuit design of increasing scale and predictability 1 . The design-build-test cycle has been exemplified by the SBOL stack [2][3][4] and SynBioHub 5 , which allow a standardized description for designs using a common language to represent parts and circuits. The SBOL stack and associated tools provide for creation 6 and storage of these designs, their sequences and the experiments related to their characterization 7 and performance, as well as models to allow simulation 8 .…”

mentioning

confidence: 99%

Phase space characterization for gene circuit design

Silva

Matute

Núñez

et al. 2019

Preprint

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Genetic circuit design requires characterization of the dynamics of synthetic gene expression. This is a difficult problem since gene expression varies in complex ways over time and across different contexts. Here we present a novel method for characterizing the dynamics of gene expression with a few parameters that account for changes in cellular context (host cell physiology) and compositional context (adjacent genes). The dynamics of gene circuits were characterized by a trajectory through a multi-dimensional phase space parameterized by the expression levels of each of their constituent transcriptional units (TU). These trajectories followed piecewise linear dynamics, with each dynamical regime corresponding to different growth regimes, or cellular contexts. Thus relative expression rates were changed by transitions between growth regimes, but were constant in each regime. We present a plausible two-factor mathematical model for this behavior based on resource consumption. By analyzing different combinations of TUs, we then showed that relative expression rates were significantly affected by the neighboring TU (compositional context), but maintained piecewise linear dynamics across cellular and compositional contexts. Taken together these results show that TU expression dynamics could be predicted by a reference TU up to a context dependent scaling factor. This model provides a framework for design of genetic circuits composed of TUs. A common sharable reference TU may be chosen and measured in the cellular contexts of interest. The output of each TU in the circuit may then be predicted from a simple function of the output of the reference TU in the given cellular context. This will aid in genetic circuit design by providing simple models for the dynamics of gene circuits and their constituent TUs.

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Synthetic Biology Open Language (SBOL) Version 2.2.0

Cited by 27 publications

References 0 publications

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

Organizing genome engineering for the gigabase scale

Phase space characterization for gene circuit design

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