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Lookup NU author(s): Jonny Naylor, Dr Harold Fellermann, Professor Natalio KrasnogorORCiD
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Motivation3D physical modelling is a powerful computational technique that allows for the simulation of complex systems such as consortia of mixed bacterial species. The complexities in physical modelling reside in the knowledge intensive model building process and the computational expense in calculating their numerical solutions. These models can offer insights into microbiology, both in understanding natural systems and as design tools for developing novel synthetic bacterial systems. Developing a robust synthetic system typically requires multiple iterations around the specify→design→build→test cycle to meet specifications. This process is laborious and expensive for both the computational and laboratory aspects, hence any improvement in any of the workflow steps would be welcomed. We have previously introduced Simbiotics, a powerful and flexible platform for designing and analyzing 3D simulations of mixed species bacterial populations. Simbiotics requires programming experience to use which creates barriers to entry for use of the tool.ResultsIn the spirit of enabling biologists who may not have programming skills to install and utilize Simbiotics, we present in this application note Easybiotics, a user-friendly graphical user interface for Simbiotics. Users may design, simulate and analyze models from within the graphical user interface, with features such as live graph plotting and parameter sweeps. Easybiotics provides full access to all of Simbiotics simulation features, such as cell growth, motility and gene regulation.Availability and implementationEasybiotics and Simbiotics are free to use under the GPL3.0 licence, and can be found at: http://ico2s.org/software/simbiotics.html. We also provide readily downloadable virtual machine sandboxes to facilitate rapid installation.
Author(s): Naylor J, Fellermann H, Krasnogor N
Publication type: Article
Publication status: Published
Journal: Bioinformatics
Year: 2019
Volume: 35
Issue: 19
Pages: 3859-3860
Print publication date: 01/10/2019
Online publication date: 23/02/2019
Acceptance date: 21/02/2019
ISSN (print): 1367-4803
ISSN (electronic): 1460-2059
Publisher: Oxford University Press
URL: https://doi.org/10.1093/bioinformatics/btz131
DOI: 10.1093/bioinformatics/btz131
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