NIHBA: A network interdiction approach for metabolic engineering design

Shouyong Jiang; Yong Wang; Marcus Kaiser; Natalio Krasnogor

doi:10.1093/bioinformatics/btaa163

NIHBA: A network interdiction approach for metabolic engineering design

Shouyong Jiang^*, Yong Wang, Marcus Kaiser, Natalio Krasnogor

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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Abstract

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). Contact: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk

Original language	English
Pages (from-to)	3482-3492
Number of pages	11
Journal	Bioinformatics
Volume	36
Issue number	11
Early online date	13 Mar 2020
DOIs	https://doi.org/10.1093/bioinformatics/btaa163
Publication status	Published - 1 Jun 2020

Bibliographical note

Funding Information:
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project ‘Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)’. N.K. was funded by a Royal Academy of Engineering Chair in Emerging Technology award.

Data Availability Statement

The data and software used and the tool developed are all available online:

GEM model: iML1515 from BIGG database (bigg.ucsd.edu).

Simulation software: Cobra toolbox 3.0 (https://opencobra.github.io/).

MILP solver: http://www.gurobi.com/.

NIHBA: https://github.com/chang88ye/NIHBA.

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10.1093/bioinformatics/btaa163Licence: CC BY

Jiang_etal_Bioinfo_NIHBA_VOR
The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Final published version, 1.18 MBLicence: CC BY

Cite this

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title = "NIHBA: A network interdiction approach for metabolic engineering design",

abstract = "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). Contact: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk ",

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N2 - 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). Contact: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk

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NIHBA: A network interdiction approach for metabolic engineering design

Abstract

Bibliographical note

Data Availability Statement

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