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Data Integration and Mining for Synthetic Biology Design

Lookup NU author(s): Dr Goksel Misirli, Dr Jennifer Hallinan, Dr Phillip Lord, James Alastair McLaughlin McLaughlin, Professor Anil Wipat

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

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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