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    A standard-enabled workflow for synthetic biology

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    Type
    Article
    Authors
    Myers, Chris J.
    Beal, Jacob
    Gorochowski, Thomas E.
    Kuwahara, Hiroyuki cc
    Madsen, Curtis
    McLaughlin, James Alastair
    Mısırlı, Göksel
    Nguyen, Tramy
    Oberortner, Ernst
    Samineni, Meher
    Wipat, Anil
    Zhang, Michael
    Zundel, Zach
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Date
    2017-06-15
    Online Publication Date
    2017-06-15
    Print Publication Date
    2017-06-15
    Permanent link to this record
    http://hdl.handle.net/10754/625613
    
    Metadata
    Show full item record
    Abstract
    A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.
    Citation
    Myers CJ, Beal J, Gorochowski TE, Kuwahara H, Madsen C, et al. (2017) A standard-enabled workflow for synthetic biology. Biochemical Society Transactions 45: 793–803. Available: http://dx.doi.org/10.1042/bst20160347.
    Sponsors
    This material is based on work supported by the National Science Foundation under grant nos CCF-1218095 and DBI-135604. T.E.G. is supported by BrisSynBio, a Biotechnology and Biological Sciences Research Council and Engineering and Physical Sciences Research Council Synthetic Biology Research Centre [BB/L01386X/1]. G.M. and A.W. have been supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant EP/J02175X/1]. J.A.M. is supported by FUJIFILM DioSynth Biotechnologies. J.B. is supported, in part, by the National Science Foundation Expeditions in Computing Program Award #1522074 as part of the Living Computing Project. E.O. is supported under Contract No. DE-AC02-05CH11231 by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility. This document does not contain technology or technical data controlled under either the U.S. International Traffic in Arms Regulations or the U.S. Export Administration Regulations.
    Publisher
    Portland Press Ltd.
    Journal
    Biochemical Society Transactions
    DOI
    10.1042/bst20160347
    PubMed ID
    28620041
    Additional Links
    http://www.biochemsoctrans.org/content/45/3/793
    ae974a485f413a2113503eed53cd6c53
    10.1042/bst20160347
    Scopus Count
    Collections
    Articles; Computational Bioscience Research Center (CBRC)

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