Multi-objective experimental design for (13)C-based metabolic flux analysis.
(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis.
Bio- & chemical systems Technology, Reactor Engineering and Safety Section, Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium.
Jeroen
J
Bouvin
Bio- & chemical systems Technology, Reactor Engineering and Safety Section, Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium.
Simon
S
Cajot
Department of Industrial Science and Technology, UC Leuven-Limburg, Agoralaan Building B, 3590 Diepenbeek, Belgium.
Pieter-Jan
PJ
D'Huys
Division of Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Leuven, Belgium.
Jerry
J
Ampofo-Asiama
Laboratory of Molecular Bacteriology, Department of Microbiology and Immunology, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.
Jozef
J
Anné
Chemical and Biochemical Process Technology and Control section, Technology Campus Ghent, Gebroeders De Smetstraat 1, 9000 Gent, Belgium.
Jan
J
Van Impe
Division of Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Leuven, Belgium.
Annemie
A
Geeraerd
Bio- & chemical systems Technology, Reactor Engineering and Safety Section, Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium. Electronic address: kristel.bernaerts@cit.kuleuven.be.
Kristel
K
Bernaerts
D002247:Carbon Isotopes; D045744:Cell Line, Tumor; D006801:Humans; D064688:Metabolic Flux Analysis / Q000379:methods*; D012107:Research Design; D048170:Streptomyces lividans
D002247:Carbon Isotopes
(13)C-based metabolic flux analysis; Carcinoma cell line; Central carbon metabolism; Cost-effective experimental design; Multi-objective optimal experimental design; Streptomyces lividans