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Lookup NU author(s): Dr Shouyong Jiang, Professor Marcus Kaiser, Professor Natalio KrasnogorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.MOTIVATION: Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION: Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Author(s): Jiang S, Wang Y, Kaiser M, Krasnogor N
Publication type: Article
Publication status: Published
Journal: Bioinformatics
Year: 2020
Volume: 36
Issue: 11
Pages: 3482-3492
Online publication date: 13/03/2020
Acceptance date: 10/03/2020
Date deposited: 18/06/2020
ISSN (print): 1367-4803
ISSN (electronic): 1460-2059
Publisher: Oxford University Press
URL: https://doi.org/10.1093/bioinformatics/btaa163
DOI: 10.1093/bioinformatics/btaa163
PubMed id: 32167529
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