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Multiobjective flux balancing using the NISE method for metabolic network analysis
Young-Gyun Oh1, Dong-Yup Lee, Sang Yup Lee
1Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.
Biotechnology Progress
|July 3, 2009
Summary
This study introduces a new multiobjective flux balance analysis (FBA) method using noninferior set estimation (NISE) to efficiently analyze metabolic engineering objectives. It accelerates strain improvement by reducing computation time for Pareto curves and flux distribution analysis.
Area of Science:
- Metabolic Engineering
- Systems Biology
- Computational Biology
Background:
- Flux balance analysis (FBA) is a key tool for metabolic engineering but struggles with multiobjective optimization.
- Conflicting objectives in metabolic networks require advanced analytical approaches.
Purpose of the Study:
- To develop a novel multiobjective flux balance analysis (FBA) method.
- To adapt the noninferior set estimation (NISE) method for multiobjective linear programming (MOLP) problems in metabolic networks.
- To enable efficient analysis of Pareto curves and flux distributions for complex metabolic engineering challenges.
Main Methods:
- Adaptation of the noninferior set estimation (NISE) method for multiobjective FBA.
- Generation of Pareto curves for conflicting objectives without redundant single-objective optimizations.
- Application to a genome-scale in silico model of E. coli for poly(3-hydroxybutyrate) [P(3HB)] production.
Main Results:
- The NISE-adapted method efficiently approximates Pareto curves for conflicting objectives.
- Flux distributions at Pareto optimal solutions were obtained, revealing internal metabolic network changes.
- Analysis of succinic acid vs. biomass production in E. coli identified relationships and facilitated in silico analysis of knockout strains.
Conclusions:
- The proposed multiobjective FBA method accelerates metabolic engineering by reducing computation time.
- It provides a powerful approach for understanding trade-offs between multiple objectives in metabolic networks.
- This method enhances strain improvement strategies through efficient Pareto curve generation and flux analysis.
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