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Lookup NU author(s): Matthew Crowther, Professor Anil Wipat, Dr Angel Goni-Moreno
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2022 American Chemical Society. All rights reserved. As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.
Author(s): Crowther M, Wipat A, Goni-Moreno A
Publication type: Article
Publication status: Published
Journal: ACS Synthetic Biology
Year: 2022
Volume: 11
Issue: 9
Pages: 3058-3066
Print publication date: 16/09/2022
Online publication date: 31/08/2022
Acceptance date: 02/04/2018
Date deposited: 03/10/2022
ISSN (electronic): 2161-5063
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acssynbio.2c00255
DOI: 10.1021/acssynbio.2c00255
PubMed id: 36044984
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