A genetic circuit compiler : generating combinatorial genetic circuits with web semantics and inference

Waites, William and Misirli, Göksel and Cavaliere, Matteo and Danos, Vincent and Wipat, Anil (2018) A genetic circuit compiler : generating combinatorial genetic circuits with web semantics and inference. ACS Synthetic Biology, 7 (12). pp. 2812-2823. ISSN 2161-5063 (https://doi.org/10.1021/acssynbio.8b00201)

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Abstract

A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating ?-language simulations from semantic descriptions of genetic circuits.

ORCID iDs

Waites, William ORCID logoORCID: https://orcid.org/0000-0002-7759-6805, Misirli, Göksel, Cavaliere, Matteo, Danos, Vincent and Wipat, Anil;
  • Item type:Article
    ID code:84516
    Dates:
    Date
    Event
    21 December 2018
    Published
    8 November 2018
    Published Online
    8 November 2018
    Accepted
    Notes:Funding Information: Thanks to Michael Korbakov for porting the original Haskell implementation of the agent declaration code to Python. We would also like to thank the anonymous reviewers, whose feedback has resulted in a much improved manuscript. W.W., M.C., G.M., A.W., and V.D. acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J02175X/1 and from UK Research Councils' Synthetic Biology for Growth program. W.W. and V.D. acknowledge the European Union's Seventh Framework Programme for research, technological development and demonstration grant number 320823 (to V.D. and W.W.). W.W. also acknowledges support from the National Academies Keck Futures Initiative of the National Academy of Sciences award number NAKFI CB12. Funding Information: Thanks to Michael Korbakov for porting the original Haskell implementation of the agent declaration code to Python. We would also like to thank the anonymous reviewers, whose feedback has resulted in a much improved manuscript. W.W., M.C., G.M., A.W., and V.D. acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J02175X/1 and from UK Research Councils’ Synthetic Biology for Growth program. W.W. and V.D. acknowledge the European Union’s Seventh Framework Programme for research, technological development and demonstration grant number 320823 (to V.D. and W.W.). W.W. also acknowledges support from the National Academies Keck Futures Initiative of the National Academy of Sciences award number NAKFI CB12. Publisher Copyright: © 2018 American Chemical Society. ACS Synth. Biol. 2018, 7, 12, 2812–2823
    Subjects:Science > Natural history > Biology
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:02 Mar 2023 14:47
    Last modified:11 Nov 2024 13:48
    URI:https://strathprints.strath.ac.uk/id/eprint/84516
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