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

William Waites; Göksel Misirli; Matteo Cavaliere; Vincent Danos; Anil Wipat

doi:10.1021/acssynbio.8b00201

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

William Waites^*
, Göksel Misirli
, Matteo Cavaliere
, Vincent Danos
, Anil Wipat

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

5 Downloads (Pure)

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.

Original language	English
Pages (from-to)	2812-2823
Number of pages	12
Journal	ACS Synthetic Biology
Volume	7
Issue number	12
Early online date	8 Nov 2018
DOIs	https://doi.org/10.1021/acssynbio.8b00201
Publication status	Published - 21 Dec 2018

Funding

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. 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.

Keywords

genetic circuits
inference
program generation
semantic web
synthetic biology
web semantics
biological machines
Genetic Circuit Compiler (GCC)

Access to Document

10.1021/acssynbio.8b00201Licence: CC BY 4.0

Waites-etal-ACS-SB-2018-A-genetic-circuit-compilerFinal published version, 4.74 MBLicence: CC BY 4.0

Cite this

@article{532d6760dc0d4b14b81adeda62765876,

title = "A genetic circuit compiler: generating combinatorial genetic circuits with web semantics and inference",

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.",

keywords = "genetic circuits, inference, program generation, semantic web, synthetic biology, web semantics, biological machines, Genetic Circuit Compiler (GCC)",

author = "William Waites and G{\"o}ksel Misirli and Matteo Cavaliere and Vincent Danos and Anil Wipat",

note = "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{\textquoteright} Synthetic Biology for Growth program. W.W. and V.D. acknowledge the European Union{\textquoteright}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: {\textcopyright} 2018 American Chemical Society. ACS Synth. Biol. 2018, 7, 12, 2812–2823",

year = "2018",

month = dec,

day = "21",

doi = "10.1021/acssynbio.8b00201",

language = "English",

volume = "7",

pages = "2812--2823",

journal = "ACS Synthetic Biology",

issn = "2161-5063",

publisher = "American Chemical Society",

number = "12",

}

TY - JOUR

T1 - A genetic circuit compiler

T2 - generating combinatorial genetic circuits with web semantics and inference

AU - Waites, William

AU - Misirli, Göksel

AU - Cavaliere, Matteo

AU - Danos, Vincent

AU - Wipat, Anil

N1 - 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

PY - 2018/12/21

Y1 - 2018/12/21

N2 - 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.

AB - 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.

KW - genetic circuits

KW - inference

KW - program generation

KW - semantic web

KW - synthetic biology

KW - web semantics

KW - biological machines

KW - Genetic Circuit Compiler (GCC)

U2 - 10.1021/acssynbio.8b00201

DO - 10.1021/acssynbio.8b00201

M3 - Article

C2 - 30408409

AN - SCOPUS:85057571288

SN - 2161-5063

VL - 7

SP - 2812

EP - 2823

JO - ACS Synthetic Biology

JF - ACS Synthetic Biology

IS - 12

ER -

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

Abstract

Funding

Keywords

Access to Document

Fingerprint

Cite this