Steady-state Optimization of Biochemical Systems by Bi-level Programming

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Accession number: 20172603848930
Authors: Xu, Gongxian 1   ; Li, Yang 1
Author affiliation : 1 Department of Mathematics, Bohai University, Jinzhou; 121013, China
Corresponding author: Xu, Gongxian (gxxu@bhu.edu.cn)
Source title: Computers and Chemical Engineering
Abbreviated source title: Comput. Chem. Eng.
Volume: 106
Issue date: 2017
Publication Year: 2017
Pages: 286-296
Language: English
ISSN: 00981354
CODEN: CCENDW
Document type: Journal article (JA)
Publisher: Elsevier Ltd
Abstract: A new method is proposed for the steady-state optimization of biochemical systems described by Generalized Mass Action (GMA) models. In this method, a bi-level programming with a two-layer nested structure is established. In this bi-level problem, the upper-level objective is to maximize a flux or a metabolite concentration, and the lower-level objective is to minimize the total sum of metabolite concentrations of biochemical systems. The biological significance of the presented bi-level programming problem is to maximize the production rate or concentration of the desired product under a minimal metabolic cost to the biochemical system. To efficiently solve the above NP-hard, non-convex and nonlinear bi-level programming problem, we reformulate it into a single-level optimization problem by using appropriate transformation strategies. The proposed framework is applied to four case studies and has shown the tractability and effectiveness of the method. A comparison of our proposed method and other methods is also given. © 2017 Elsevier Ltd
Number of references: 59
Main heading: Optimization
Controlled terms: Algorithms -  Biochemistry -  Mathematical transformations -  Metabolites
Uncontrolled terms: Bi-level problems -  Bi-level programming -  Biochemical systems -  Biological significance -  Generalized mass -  Metabolite concentrations -  Optimization problems -  Steady-state optimization
Classification code: 801.2Biochemistry  -  921.3Mathematical Transformations  -  921.5Optimization Techniques
DOI: 10.1016/j.compchemeng.2017.06.019
Funding Details: Number;  Acronym;  Sponsor:  11101051;  NSFC;  National Natural Science Foundation of China  
Number;  Acronym;  Sponsor:  11371071;  NSFC;  National Natural Science Foundation of China  
Number;  Sponsor:  2015020038;  Natural Science Foundation of Liaoning Province  
Database: Compendex
Compilation and indexing terms, © 2017 Elsevier Inc，