NIHBA: A Network Interdiction Approach for Metabolic Engineering Design
release_uokyr5vb5rgjvaekp25ilptc24
by
Shouyong Jiang, Yong Wang, Marcus Kaiser, Natalio Krasnogor
Abstract
<jats:title>Abstract</jats:title>
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<jats:title>Motivation</jats:title>
Flux balance analysis (FBA) based bilevel optimisation 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 optimisation 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.
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<jats:title>Results</jats:title>
Here, we have employed a network interdiction (NI) model free of growth optimality assumptions, a special case of bilevel optimisation, 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 hour for examples studied in this paper).
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<jats:title>Availability</jats:title>
Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA.
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<jats:title>Supplementary information</jats:title>
Supplementary data are available at Bioinformatics online.
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