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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. |
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