The identification of enzyme targets for the optimization of a valine producing Corynebacterium glutamicum strain using a kinetic model

• Published in: Biotechnology progress 2009-05, Vol.25 (3), p.754-762
• Main Authors: Magnus, Jørgen Barsett, Oldiges, Marco, Takors, Ralf
• Format: Article
• Language: English
• Subjects: Bacterial Proteins - chemistry; Bacterial Proteins - genetics; Bacterial Proteins - metabolism; Biological and medical sciences; Biotechnology; Corynebacterium glutamicum; Corynebacterium glutamicum - chemistry; Corynebacterium glutamicum - enzymology; Corynebacterium glutamicum - genetics; Corynebacterium glutamicum - metabolism; Fundamental and applied biological sciences. Psychology; kinetic model; Kinetics; metabolic control analysis; metabolic engineering; Models, Biological; strain optimization; Valine - metabolism; valine production
• ISSN: 8756-7938; 1520-6033
• DOI: 10.1002/btpr.184

The enzyme targets for the rational optimization of a Corynebacterium glutamicum strain constructed for valine production are identified by analyzing the control of flux in the valine/leucine pathway. The control analysis is based on measurements of the intracellular metabolite concentrations and on a kinetic model of the reactions in the investigated pathway. Data‐driven and model‐based methods are used and evaluated against each other. The approach taken gives a quantitative evaluation of the flux control and it is demonstrated how the understanding of flux control is used to reach specific recommendations for strain optimization. The flux control coefficients (FCCs) with respect to the valine excretion rate were calculated, and it was found that the control is distributed mainly between the acetohydroxyacid synthase enzyme (FCC = 0.32), the branched chain amino acid transaminase (FCC = 0.27), and the exporting translocase (FCC = 0.43). The availability of the precursor pyruvate has substantial influence on the valine flux, whereas the cometabolites are less important as demonstrated by the calculation of the respective response coefficients. The model is further used to make in‐silico predictions of the change in valine flux following a change in enzyme level. A doubling of the enzyme level of valine translocase will result in an increase in valine flux of 31%. By optimizing the enzyme levels with respect to valine flux it was found that the valine flux can be increased by a factor 2.5 when the optimal enzyme levels are implemented. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009