Keele Research Repository
Explore the Repository
Misirli, G, Wates, W, Cavaliere, M, Danos, V and Wiipat, A (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
acssynbio.8b00201.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.
Download (450kB) | Preview
acs_composing_biological_parts_supplementary.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.
Download (570kB) | Preview
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 k-language simulations from semantic descriptions of genetic circuits.
Item Type: | Article |
---|---|
Additional Information: | The final version of this accepted manuscript can be accessed online at https://pubs.acs.org/doi/10.1021/acssynbio.8b00201 |
Uncontrolled Keywords: | semantic web; inference; program generation; synthetic biology; genetic circuits |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Natural Sciences > School of Computing and Mathematics |
Depositing User: | Symplectic |
Date Deposited: | 09 Nov 2018 16:51 |
Last Modified: | 08 Nov 2019 01:30 |
URI: | https://eprints.keele.ac.uk/id/eprint/5495 |