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Gsmodutils: a python based framework for test-driven genome scale metabolic model development

Gilbert, James; Pearcy, Nicole; Norman, Rupert; Millat, Thomas; Winzer, Klaus; King, John; Hodgman, Charlie; Minton, Nigel; Twycross, Jamie

Gsmodutils: a python based framework for test-driven genome scale metabolic model development Thumbnail


James Gilbert

Nicole Pearcy

Rupert Norman

Thomas Millat

Professor of Theoretical Mechanics

Charlie Hodgman


Russell Schwartz


© 2019 The Author(s) 2019. Published by Oxford University Press. Motivation: Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle. Results: As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions. Availability and implementation: The software framework described within this paper is open source and freely available from Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article Type Article
Acceptance Date Jan 31, 2019
Online Publication Date Feb 13, 2019
Publication Date Sep 15, 2019
Deposit Date Feb 14, 2019
Publicly Available Date Feb 14, 2019
Journal Bioinformatics
Print ISSN 1367-4803
Electronic ISSN 1460-2059
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 35
Issue 18
Pages 3397-3403
Keywords Statistics and Probability; Computational Theory and Mathematics; Biochemistry; Molecular Biology; Computational Mathematics; Computer Science Applications
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