TY - JOUR
T1 - NIHBA
T2 - A network interdiction approach for metabolic engineering design
AU - Jiang, Shouyong
AU - Wang, Yong
AU - Kaiser, Marcus
AU - Krasnogor, Natalio
N1 - 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.
PY - 2020/6/1
Y1 - 2020/6/1
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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85085904304&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa163
DO - 10.1093/bioinformatics/btaa163
M3 - Article
C2 - 32167529
AN - SCOPUS:85085904304
VL - 36
SP - 3482
EP - 3492
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 11
ER -