How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics
S Niedenführ, W Wiechert, K Nöh - Current opinion in biotechnology, 2015 - Elsevier
S Niedenführ, W Wiechert, K Nöh
Current opinion in biotechnology, 2015•ElsevierHighlights•Tracer experiments, analytics and modeling constitute the 13 C fluxomics
triumvirate.•13 C fluxomics tools differ in complexity, information power and universality.•The
biological question prescribes the choice of the modeling approach.•In turn, modeling poses
unique challenges for experimental and analytical setups.•Standards will raise throughput
and flexibility of 13 C fluxomics.Metabolic reaction rates (fluxes) contribute fundamentally to
our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable …
triumvirate.•13 C fluxomics tools differ in complexity, information power and universality.•The
biological question prescribes the choice of the modeling approach.•In turn, modeling poses
unique challenges for experimental and analytical setups.•Standards will raise throughput
and flexibility of 13 C fluxomics.Metabolic reaction rates (fluxes) contribute fundamentally to
our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable …
Highlights
- Tracer experiments, analytics and modeling constitute the 13 C fluxomics triumvirate.
- 13 C fluxomics tools differ in complexity, information power and universality.
- The biological question prescribes the choice of the modeling approach.
- In turn, modeling poses unique challenges for experimental and analytical setups.
- Standards will raise throughput and flexibility of 13 C fluxomics.
Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to ‘measure’the in vivo fluxes within an organism that are not directly observable. In recent years, 13 C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific ‘travelers’ who are striving to resolve research questions with the currently available 13 C fluxomics toolset.
Elsevier
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