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Title: Knowledge Representation in Synthetic Biology
Authors: McLaughlin, James Alastair
Issue Date: 2019
Publisher: Newcastle University
Abstract: Synthetic biology, or SynBio, is a relatively new and exciting field concerning the formalisation of genetic engineering into a design, build, test, learn lifecycle common to other engineering disciplines. This lifecycle can be used to systematically develop biological systems, such as synthetic genetic circuits — where transcriptional machinery is repurposed to construct familiar electronic circuit concepts such as logic gates — and other engineered devices such as biosensors or drug production factories. Synthetic biological systems are typically designed by repurposing existing natural and synthetic biological parts. This design process is made possible by knowledge about part structure and function, which can be experimentally derived or predicted using bioinformatics methodologies. However, the process of gathering such knowledge is arduous, as it is often computationally intractable, distributed across multiple disparate databases with semantic and syntactic heterogeneity, or even not recorded at all. The research question motivating this work is how the machine-tractability of knowledge can be improved in order to make the synthetic biology design process more efficient. There are both short-term and long-term approaches. The short-term approach is to improve the ease of access and machine-tractability of existing knowledge relevant to SynBio design. The long-term approach is to establish the software and data infrastructure necessary to enable knowledge about future designs to be documented in a standardized manner. This work investigates both approaches with research into data standards, significantly furthering the development of the Synthetic Biology Open Language (SBOL) to improve the machine-tractability of design knowledge; the research and development of novel technology for data integration to make existing information easier to access; conversion of an existing dataset, the iGEM Registry, into an enriched SBOL representation; the development of SynBioHub, a repository for the sharing and dissemination of future SynBio designs; and SynBioCAD, a visual tool enabling synthetic biologists to capture their designs using data standards.
Description: PhD Thesis
Appears in Collections:School of Computing

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