Applied Microbiology and Biotechnology

, Volume 86, Issue 5, pp 1243–1255 | Cite as

Metabolic flux distributions: genetic information, computational predictions, and experimental validation

  • Lars M. Blank
  • Lars Kuepfer


Flux distributions in intracellular metabolic networks are of immense interest to fundamental and applied research, since they are quantitative descriptors of the phenotype and the operational mode of metabolism in the face of external growth conditions. In particular, fluxes are of relevance because they do not belong to the cellular inventory (e.g., transcriptome, proteome, metabolome), but are rather quantitative moieties, which link the phenotype of a cell to the specific metabolic mode of operation. A frequent application of measuring and redirecting intracellular fluxes is strain engineering, which ultimately aims at shifting metabolic activity toward a desired product to achieve a high yield and/or rate. In this article, we first review the assessment of intracellular flux distributions by either qualitative or rather quantitative computational methods and also discuss methods for experimental measurements. The tools at hand will then be exemplified on strain engineering projects from the literature. Finally, the achievements are discussed in the context of future developments in Metabolic Engineering and Synthetic Biology.


Flux balance analysis Metabolic network analysis 13C-metabolic flux analysis Synthetic biology Metabolic engineering Industrial biotechnology 



We thank Birgitta Ebert for the valuable comments on the manuscript and Jana Rühl for critically reading the genetic information section. LMB acknowledges the ongoing support of Andreas Schmid. We apologize to all researchers who also contributed significantly to the advances of metabolic flux analysis and prediction, but were not mentioned due to space restrictions.


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Authors and Affiliations

  1. 1.Laboratory of Chemical Biotechnology, Faculty of Biochemical and Chemical EngineeringTU DortmundDortmundGermany
  2. 2.Competence Center Systems Biology and Computational SolutionsBayer Technology Services GmbHLeverkusenGermany

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