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Mechanistic model for prediction of formate dehydrogenase kinetics under industrially relevant conditions
Author(s) -
Schmidt T.,
Michalik C.,
Zavrel M.,
Spieß A.,
Marquardt W.,
AnsorgeSchumacher M. B.
Publication year - 2009
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.282
Subject(s) - formate dehydrogenase , formate , chemistry , extrapolation , cofactor , biocatalysis , kinetics , biochemical engineering , decomposition , substrate (aquarium) , reaction rate , catalysis , combinatorial chemistry , biological system , thermodynamics , reaction mechanism , enzyme , biochemistry , organic chemistry , mathematics , biology , mathematical analysis , ecology , physics , quantum mechanics , engineering
Formate dehydrogenase (FDH) from Candida boidinii is an important biocatalyst for the regeneration of the cofactor NADH in industrial enzyme‐catalyzed reductions. The mathematical model that is currently applied to predict progress curves during (semi‐)batch reactions has been derived from initial rate studies. Here, it is demonstrated that such extrapolation from initial reaction rates to performance during a complete batch leads to considerable prediction errors. This observation can be attributed to an invalid simplification during the development of the literature model. A novel mechanistic model that describes the course and performance of FDH‐catalyzed NADH regeneration under industrially relevant process conditions is introduced and evaluated. Based on progress curve instead of initial reaction rate measurements, it was discriminated from a comprehensive set of mechanistic model candidates. For the prediction of reaction courses on long time horizons (>1 h), decomposition of NADH has to be considered. The model accurately describes the regeneration reaction under all conditions, even at high concentrations of the substrate formate and thus is clearly superior to the existing model. As a result, for the first time, course and performance of NADH regeneration in industrial enzyme‐catalyzed reductions can be accurately predicted and used to optimize the cost efficiency of the respective processes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010