Identifying the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae under different culture conditions

•Shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae.•Identifying the shared metabolic objectives under different culture conditions.•Mul-level programming model for achieving this identification goal is proposed.•An efficient method is proposed to solve the presented mul-...

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Veröffentlicht in:Journal of biotechnology Jg. 248; S. 59 - 68
Hauptverfasser: Xu, Gongxian, Li, Caixia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 20.04.2017
Schlagworte:
1,3-propanediol
Algorithms
biotransformation
Computational Biology
Flux balance analysis
glycerol
Glycerol - metabolism
Klebsiella pneumoniae
Klebsiella pneumoniae - metabolism
linear programming
Metabolic Flux Analysis
Metabolic Networks and Pathways
Metabolic objective identification
metabolism
Models, Biological
Multi-level programming
Optimization
Propylene Glycols - analysis
Propylene Glycols - metabolism
Multi-level programming
Flux balance analysis
Metabolic objective identification
1,3-propanediol
Optimization
Klebsiella pneumoniae
ISSN:0168-1656, 1873-4863, 1873-4863
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Zusammenfassung:•Shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae.•Identifying the shared metabolic objectives under different culture conditions.•Mul-level programming model for achieving this identification goal is proposed.•An efficient method is proposed to solve the presented mul-level programming.•We obtain the shared metabolic objective under three groups of experimental data. This paper addresses the problem of identifying the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae for production of 1,3-propanediol (1,3-PD) under different culture conditions. To achieve this goal, we propose a multi-level programming model. This model includes three optimization problems, where the constraint region of the first level problem is implicitly determined by the other two optimization problems. The optimized objectives of the first and second level problems are to minimize the set of fluxes that are of major importance to glycerol metabolism and the difference between the observed fluxes and those computed by the model, respectively. The third level problem in the proposed multi-level programming simultaneously solves a set of flux balance analysis (FBA) models. A method is proposed to solve efficiently the presented multi-level programming problem. In this method, we first transform the proposed multi-level problem into a bi-level problem by applying the dual theory of linear programming to the FBA models of the third level. Next, the optimal solution of the above bi-level problem is obtained by iteratively solving a sequence of mixed integer programming problems. Optimization results reveal that the proposed method can identify the shared metabolic objectives of glycerol bioconversion in Klebsiella pneumoniae under three groups of experimental data.
Bibliographie:ObjectType-Article-1
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content type line 23
ISSN:0168-1656
1873-4863
1873-4863
DOI:10.1016/j.jbiotec.2017.03.014