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Multi-objective experimental design for (13)C-based metabolic flux analysis
Jeroen Bouvin1, Simon Cajot1, Pieter-Jan D'Huys2
1Bio- & chemical systems Technology, Reactor Engineering and Safety Section, Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium.
Mathematical Biosciences
|August 13, 2015
Summary
Designing cost-effective ¹³C-tracer experiments for metabolic flux analysis is crucial. This study presents a framework for optimal tracer mixture design, balancing accuracy and cost for biological networks.
Area of Science:
- Metabolic Engineering and Systems Biology
- Biotechnology and Bioprocess Engineering
- Computational Biology and Bioinformatics
Background:
- (13)C-based metabolic flux analysis (MFA) is a powerful tool for understanding central carbon metabolism.
- High costs associated with specialized (13)C-labeled tracers can limit experimental feasibility.
- Optimizing tracer composition is essential for maximizing data quality while minimizing expenses.
Purpose of the Study:
- To develop a cost-effective framework for designing (13)C-tracer experiments in metabolic flux analysis.
- To compute optimal tracer mixtures for different biological networks, including Streptomyces lividans and carcinoma cell lines.
- To evaluate and compare linear and non-linear experimental design criteria for MFA.
Main Methods:
- Development of a framework for cost-effective design of (13)C-tracer experiments.
- Computation of linear (D-criterion) and non-linear (S-criterion) optimal input mixtures.
- Application of multi-objective optimization to balance experimental quality and cost.
Main Results:
- Optimal tracer mixtures for carcinoma cell lines and S. lividans often include high amounts of 1,2-(13)C2 glucose and uniformly labeled glucose.
- Linear and non-linear design approaches yield similar optimal mixtures, with the linear approach favored for lower computational cost.
- Multi-objective optimization identified cost-effective tracer combinations, such as specific glucose and glutamine labeling strategies, reducing experimental expenses.
Conclusions:
- A multi-objective linear approach effectively optimizes experimental designs for (13)C-MFA, even for non-linear problems.
- The proposed framework and tools facilitate high-throughput screening of (13)C-tracers and stimulate wider application of optimal design in the field.
- Cost-effective experimental design is achievable through careful selection and mixture optimization of (13)C-labeled substrates.
Keywords:
(13)C-based metabolic flux analysisCarcinoma cell lineCentral carbon metabolismCost-effective experimental designMulti-objective optimal experimental designStreptomyces lividans
