ePrints

SBOL-OWL: An Ontological Approach for Formal and Semantic Representation of Synthetic Biology Information

Lookup NU author(s): Dr Goksel Misirli, Dr Angel Goni-Moreno, James Alastair McLaughlin McLaughlin, Professor Christopher Myers, Dr Phillip Lord, Professor Anil Wipat

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

© 2019 American Chemical Society.Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies.

Publication metadata

Author(s): Mısırlı G, Taylor R, Goñi-Moreno A, McLaughlin JA, Myers C, Gennari JH, Lord P, Wipat A

Publication type: Article

Publication status: Published

Journal: ACS Synthetic Biology

Year: 2019

Volume: 8

Issue: 7

Pages: 1498-1514

Print publication date: 19/07/2019

Online publication date: 06/05/2019

Acceptance date: 20/12/2018

ISSN (electronic): 2161-5063

Publisher: American Chemical Society

URL: https://doi.org/10.1021/acssynbio.8b00532

DOI: 10.1021/acssynbio.8b00532

PubMed id: 31059645

Altmetrics

Share