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Lookup NU author(s): Dr Goksel Misirli, Dr Jennifer Hallinan, Dr Phillip Lord, James Alastair McLaughlin McLaughlin, Professor Anil Wipat
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterised parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterise biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread amongst these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single dataset, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modelling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.
Author(s): Misirli G, Hallinan J, Pocock M, Lord P, McLaughlin JA, Sauro H, Wipat A
Publication type: Article
Publication status: Published
Journal: ACS Synthetic Biology
Year: 2016
Volume: 5
Issue: 10
Pages: 1086-1097
Print publication date: 21/10/2016
Online publication date: 25/04/2016
Acceptance date: 25/04/2016
Date deposited: 13/06/2016
ISSN (electronic): 2161-5063
Publisher: American Chemical Society
URL: http://dx.doi.org/10.1021/acssynbio.5b00295
DOI: 10.1021/acssynbio.5b00295
PubMed id: 27110921
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